Method and system to detect premature ventricular contractions in cardiac activity signals

ABSTRACT

A computer implemented method and system are provided for detecting premature ventricular contractions (PVCs) in cardiac activity. The method and system obtain cardiac activity (CA) signals for a series of beats, and, for at least a portion of the series of beats, calculate QRS scores for corresponding QRS complex segments from the CA signals. The method and system calculate a variability metric for QRS scores across the series of beats, calculate a QRS complex template using QRS segments from the series of beats, calculate correlation coefficients between the QRS complex template and the QRS complex segments, compare the variability metric to a variability threshold and the correlation coefficients to a correlation threshold, and designate the CA signals to include a predetermined level of PVC burden based on the determining.

RELATED APPLICATIONS

The following applications relate to and are filed concurrently on thesame day as the present application, and are expressly incorporatedherein by reference in their entireties (hereafter referred to as“Co-Pending Related Applications”):

-   -   U.S. patent application Ser. No. ______, titled “METHOD AND        SYSTEM FOR SECOND PASS CONFIRMATION OF DETECTED CARDIAC        ARRHYTHMIC PATTERNS” (Docket No. 13215USO1),    -   U.S. patent application Ser. No. ______, titled “METHOD AND        SYSTEM TO DETECT P-WAVES IN CARDIAC ARRHYTHMIC PATTERNS” (Docket        No. 13257USO1),

U.S. patent application Ser. No. ______, titled “METHOD AND SYSTEM TODETECT R-WAVES IN CARDIAC ARRHYTHMIC PATTERNS” (Docket No. 13211USO1),and

U.S. patent application Ser. No. ______, titled “METHOD AND SYSTEM TODETECT NOISE IN CARDIAC ARRHYTHMIC PATTERNS” (Docket No. 13244USO1).

FIELD OF THE INVENTION

Embodiments herein relate generally to implantable medical devices, andmore particularly to detection and discrimination of underlyingarrhythmic events based on presence of post ventricular contractions(PVCs).

BACKGROUND OF THE INVENTION

Today, numerous atrial fibrillation (AF) detection processes areimplemented within implantable cardiac monitors (ICMs) that detectatrial fibrillation based on irregularities and variation patterns inR-wave to R-wave (RR) intervals. In some embodiments, the AF detectionprocess steps beat by beat through cardiac activity (CA) signals andanalyzes the RR intervals over a period of time. An AF episode isdeclared when the RR interval pattern for the suspect beat segments issufficiently irregular and dissimilar from RR interval patterns forsinus beat segments.

However, AF detection processes may declare false AF episodes where theICM provides a device documented AF episode, even though a patient isnot experiencing AF. False AF detection may arise due to variousconditions and behavior of the heart, such as when a patient experiencessick sinus rhythms with irregular RR intervals, experiences frequentpremature ventricular contractions (PVCs) and/or inappropriate R-wavesensing. To an extent, false AF detection is due, in part, to dependenceof the AF detection process upon identification of R-wave features, withlittle or no input concerning other features of a cardiac event. PVCs,in general, introduce unstable RR intervals, such as short-long RRintervals, where the instability may give rise to erroneous declarationof an AF episode. Thus. PVCs present a substantial challenge inconnection with AF detection algorithms that rely on RR intervalvariability.

SUMMARY

In accordance with embodiments herein, a computer implemented method isprovided for detecting premature ventricular contractions (PVCs) incardiac activity. The method is under control of one or more processorsconfigured with specific executable instructions. The method obtainscardiac activity (CA) signals for a series of beats. For at least aportion of the series of beats, the method calculates QRS scores forcorresponding QRS complex segments from the CA signals. The methodcalculates a variability metric for QRS scores across the series ofbeats, a QRS complex template using QRS segments from the series ofbeats and a correlation coefficients between the QRS complex templateand the QRS complex segments, the method compares the variability metricto a variability threshold and the correlation coefficients to acorrelation threshold and designates the CA signals to include apredetermined level of PVC burden based on the determining.

Optionally, the calculating the QRS scores may comprise calculating atleast one of a summation, area under a curve, or energy for the QRScomplex segments. The method may further comprise creating a morphologyensemble from the QRS complex segments and may compare the morphologyensemble to QRS morphologies for the corresponding QRS complex segmentsto obtain morphology correlation characteristics for the QRS complexsegments. The computing of the variability metric may includecalculating a covariance Cov_QRS from the QRS scores. The covariance maybe based on the QRS scores. The covariance Cov_QRS may represent astandard deviation of the QRS scores of the QRS complex segments dividedby an average of the QRS scores. The QRS scores may represent at leastone of amplitude sums, energy or area under the curve for thecorresponding QRS complex segments.

Optionally, the variability metric may not satisfy the variabilitythreshold, and may declare the CA signals to include an non-significantPVC burden which is less than the predetermined level of PVC burden. Themorphology ensemble may represent an ensemble average of the QRSmorphologies for a desired number of the QRS complex segments. Thecomparing may include comparing the QRS morphologies for thecorresponding QRS complex segments to the ensemble average to obtain themorphology correlation characteristics. The morphology correlationcharacteristics may represent correlations between the ensemble averageand the QRS morphologies of each of the QRS complex segments. The methodmay further comprise rejecting the beats from the CA signals thatexhibit a predetermined level of baseline drift. The variability metricmay satisfy the variability threshold and the correlation coefficientsmay satisfy the correlation conditions, declaring the CA signals to havesignificant PVC burden.

In accordance with embodiments herein, a system is provided fordetecting premature ventricular contractions (PVCs) in cardiac activity.The system comprises memory to store executable instructions. One ormore processors that, when executing the executable instructions, areconfigured to: obtain a cardiac activity (CA) signals for a series ofbeats. For at least a portion of the series of beats, the systemcalculates QRS scores for corresponding QRS complex segments from the CAsignals. The system calculates a variability metric for QRS scoresacross the series of beats, calculates a QRS complex template using theQRS complex segments and calculates correlation coefficients between theQRS complex template and the QRS complex segment. The system comparesthe variability metric to a variability threshold and the correlationcoefficients to a correlation threshold and designates the CA signals toinclude a predetermined level of PVC burden based on the determining.

Optionally, the one or more processors are configured to calculate theQRS scores comprises calculating at least one of a summation, area undera curve, or energy for the QRS complex segments. The one or moreprocessors may be configured to create a morphology ensemble from theQRS complex segments and may compare the morphology ensemble to QRSmorphologies for the corresponding QRS complex segments to obtainmorphology correlation characteristics for the QRS complex segments. Thedesignation may be based on the morphology correlation characteristics.The morphology correlation characteristic may represent an averagecorrelation coefficient. The determining may include determining whetherthe average correlation coefficient is smaller than the predeterminedthreshold. The morphology correlation characteristic may represent aminimum correlation coefficient. The determining may include determiningwhether the minimum correlation coefficient for the QRS complex segmentsis smaller than a minimum threshold.

Optionally, the morphology ensemble may represent an ensemble average ofthe QRS morphologies for a desired number of the QRS complex segments.The comparing may include comparing the QRS morphologies for thecorresponding QRS complex segments to the ensemble average to obtain themorphology correlation characteristics. The one or more processors maybe configured to compute the variability metric by calculating acovariance Cov_QRS_(SUM) from the QRS scores, the covariance based onthe QRS complex segments. The variability metric may not satisfy thevariability threshold, the system may declare the CA signals to includenon-significant PVC burden which is less than the predetermined level ofPVC burden. The correlation coefficients may not satisfy the correlationthreshold. The system may declare the CA signals do not have significantPVC burden. The variability metric may be satisfied the variabilitythreshold and the correlation coefficients satisfy the correlationthreshold, declaring the CA signals to have significant PVC burden. Whendeclaring the CA signals to have significant PVC burden, the system mayreject an original detection of AF episode, or reevaluating if the CAsignals include AF by excluding identified PVC beats.

In accordance with embodiments herein, methods and devices are providedfor determining if a CA signal contains a significant number of PVCbeats. Embodiments herein utilize an aspect that PVC beats exhibitdifferent morphology in the QRS segment compared to non-PVC beatsconducted from atria. Embodiments herein provide a PVC detection methodand device that is configured to operate in aggregate on a device signalsegments (e.g., EGM strip of determined length). The PVC detectionmethods and devices may be performed in real time by an IMD while theIMD is sensing CA activity and/or in connection with post processing innon-real time applications. The PVC methods and devices herein avoid aneed to compare each beat to a predefined sinus template, as performedby traditional PVC discrimination algorithms.

The methods and devices described herein may be implemented as a “firstpass” process to rapidly identify whether an EGM strip or arrhythmiaepisode includes a predetermined level (e.g., high) of PVC burden thatmay otherwise call into question a prior diagnosis (e.g., adetermination by an IMD that the CA signals exhibit an arrhythmiaepisode such as AF). Additionally, or alternatively, at least someembodiments may provide an adaptive template generation process in whichmorphology templates are generated “on-the-fly” during operation basedon a history of CA signals exhibited by the individual patient. Theautomatically generated templates may then be utilized to identifyspecific beats within an episode that represent PVC events. The beatsthat represent PVC events may then be flagged to be excluded fromfurther processing in connection with analyzing a series of CA signalsfor an arrhythmia.

In accordance with embodiments herein, methods and devices are describedthat may be implemented as a “second pass” or “confirmation pass” toanalyze device documented episodes. In a second pass implementation, themethods and devices herein re-analyze the CA signals previously analyzedby the IMD to determine whether the arrhythmia detection made by the IMDis true or false. PVCs tend to introduce a short RR interval followed bylong RR interval. The presence of frequent PVC beats may lead the IMD toidentify sufficient irregularities in the RR intervals which in turnleads the IMD to declare are AF episode. The methods and devices hereinallow identification of high PVC burden in the CA signal and rejectfalse detection initially declared by the IMD.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an implantable cardiac monitoring device (ICM)intended for subcutaneous implantation at a site near the heart inaccordance with embodiments herein.

FIG. 2A shows a block diagram of the ICM formed in accordance withembodiments herein.

FIG. 2B illustrates an automatic sensing control adjustment utilized bythe ORI process of the ICM in accordance with embodiments herein.

FIG. 2C illustrates cardiac activity data generated and stored by an ICMin accordance with embodiments herein.

FIG. 2D illustrates screenshots of displays in which episode statisticsand arrhythmia diagnostics may be presented to a physician in accordancewith an embodiment herein.

FIG. 2E illustrates screenshots of displays in which episode statisticsand arrhythmia diagnostics may be presented to a physician in accordancewith an embodiment herein.

FIG. 3 shows a high-level workflow for an enhanced confirmatory AFdetection process implemented in accordance with embodiments herein.

FIG. 4 illustrates a flow chart for classifying AF detection anddeveloping recommendations for sensitivity profile parameter settings inaccordance with embodiments herein.

FIG. 5 illustrates a system level diagram indicating potential devicesand networks in which the methods and systems herein may be utilized inaccordance with embodiments herein.

FIG. 6 illustrates a distributed processing system in accordance withembodiments herein.

FIG. 7 illustrates a collection of communications between the ICM, alocal device, a remote device and a server/database in accordance withembodiments herein.

FIG. 8 illustrates a process for detecting a presence of prematureventricular contractions (PVCs) within cardiac activity signals inaccordance with embodiments herein.

FIG. 9A illustrates a portion of a CA signal in connection with a singlecardiac event or beat that is analyzed in connection with the process ofFIG. 8.

FIG. 9B illustrates a CA signal that includes a series of QRS complexsegments having R-waves.

FIG. 9C illustrates multiple panels of a CA signal strip analyzed inaccordance with embodiments herein.

I. TERMS AND ABBREVIATIONS

The terms “cardiac activity signal”, “cardiac activity signals”, “CAsignal” and “CA signals” (collectively “CA signals”) are usedinterchangeably throughout to refer to an analog or digital electricalsignal recorded by two or more electrodes positioned subcutaneous orcutaneous, where the electrical signals are indicative of cardiacelectrical activity. The cardiac activity may be normal/healthy orabnormal/arrhythmic. Nonlimiting examples of CA signals include ECGsignals collected by cutaneous electrodes, and EGM signals collected bysubcutaneous electrodes.

The terms “cardiac activity data set” and “CA data set” (collectively“CA data set”) are used interchangeably to refer to a data set thatincludes measured CA signals for a series of cardiac events incombination with device documented markers.

The term “marker” refers to data and/or information identified from CAsignals that may be presented as graphical and/or numeric indiciaindicative of one or more features within the CA signals and/orindicative of one or more episodes exhibited by the cardiac events.Markers may be superimposed upon CA signals or presented proximate to,and temporally aligned with, CA signals. Non-limiting examples ofmarkers may include R-wave markers, noise markers, activity markers,interval markers, refractory markers, P-wave markers, T-wave markers,PVC markers, sinus rhythm markers, AF markers and other arrhythmiamarkers. As a further nonlimiting example, basic event markers mayinclude “AF entry” to indicate a beginning of an AF event, “in AF” toindicate that AF is ongoing, “AF exit” to indicate that AF hasterminated, “T” to indicate a tachycardia beat, “B” to indicate abradycardia beat, “A” to indicate an asystole beat, “VS” to indicate aregular sinus beat, “Tachy” to indicate a tachycardia episode, “Brady”to indicate a Bradycardia episode, “Asystole” to indicate an asystoleepisode, “Patient activated” to indicate a patient activated episode. Anactivity marker may indicate activity detected by activity sensor duringthe CA signal. Noise markers may indicate entry/start, ongoing, recoveryand exit/stop of noise. Markers may be presented as symbols, dashedlines, numeric values, thickened portions of a waveform, and the like.Markers may represent events, intervals, refractory periods, ICMactivity, and other algorithm related activity. For example, intervalmarkers, such as the R-R interval, may include a numeric valueindicating the duration of the interval. The AF markers indicate atrialfibrillation rhythmic.

The term “device documented marker” refers to markers that are declaredby an implantable cardiac monitor and/or implantable medical device. Anyor all of the foregoing examples of markers represent device documentmarkers. Markers may be declared based on numerous criteria, such assignal processing, feature detection and AF detection software andhardware within and/or operating on the implantable cardiac monitorand/or implantable medical device.

The term “FOI” refers to a feature of interest within CA signals.Nonlimiting examples of features of interest include an R-wave, P-wave,T-wave and isoelectric segments. A feature of interest may correspond toa peak of an individual R-wave, an average or median P, R or T-wave peakand the like.

The terms “beat” and “cardiac event” are used interchangeably and referto both normal or abnormal events.

The terms “normal” and “sinus” are used to refer to events, features,and characteristics of, or appropriate to, a heart's healthy or normalfunctioning.

The terms “abnormal,” or “arrhythmic” are used to refer to events,features, and characteristics of, or appropriate to, a un-healthy orabnormal functioning of the heart.

The term “real-time” refers to a time frame contemporaneous with anormal or abnormal episode occurrences. For example, a real-time processor operation would occur during or immediately after (e.g., withinminutes or seconds after) a cardiac event, a series of cardiac events,an arrhythmia episode, and the like.

The term “adaptive”, as used in connection with a sensitivity profile,sensitivity limit, sensitivity level or other sensing parameters, refersto an ability of the processes herein to modify the value of sensitivityand/or sensing parameters based on features within the CA signals. Thesensitivity profile parameters may include refractory period, startsensitivity, decay delay, sensitivity limit, slope of sensitivity decay,etc.

The term “sensitivity level”, as used herein, refers to a threshold thatan input CA signal must exceed for an implantable device to identify aQRS complex feature of interest (e.g., an R-wave). As one non-limitingexample, software may be implemented using a programmed sensitivitylevel to declare an R-wave to be detected when the input CA signalexceeds the current programmed sensitivity level In response, thesoftware declares a device documented feature (e.g., R-wave) marker. Thesensitivity level may be defined in various manners based on the natureof the CA signals. For example, when the CA signals measure electricalactivity in terms of millivolts, the sensitivity level represents amillivolt threshold. For example, when a cardiac beat with a 0.14 mVamplitude is sensed by a device hardware, and R-wave may be detectedwhen the current sensitivity level is programmed to 0.1 mV. However,when the sensitivity level is programmed to 0.15 mV or above, a cardiacbeat with an amplitude of 0.14 mV will not be detected as an R-wave.Embodiments herein determine an adaptive sensitivity limit andsensitivity profile for the sensitivity level.

The term “turn”, as used herein to refer to characteristics of a shapeor morphology of a CA signal, shall mean changes in a direction of theCA signal. For example, the CA signal may turn by changing directionfrom a signal having a positive slope to a negative slope, or from asignal having a negative slope to a positive slope. Turns may havevarious associated characteristics such as amplitude, frequency (e.g.,number of turns per unit time) and duration (e.g., an amount of time forthe signal to exceed and drop below a desired percentage of the signalpeak).

The terms “significant” and “non-significant”, when used in connectionwith describing PVC burden, refer to an amount of PVC burden that is, oris not, sufficient to cause an AF detection algorithm to declare a falsearrhythmia episode. A small number of PVC events, and/or a collection ofPVC events that are spaced substantially apart from one another overtime, may not be sufficient to be considered “significant” as the PVCevents do not cause the AF detection algorithm to declare a falsearrhythmia episode. Alternatively, when a sufficient number of PVCevents occur within a relatively short period of time, the potentialexists that the AF detection algorithm incorrectly identifies R-waveswithin the PVC events, leading to a declaration of a false arrhythmiaepisode. For example, a 30-45 second strip of EGM signals may includeone or more PVC events that cause the AF detection algorithm of an IMDto designate a false R-wave marker. Based on the number of false R-wavemarkers in the EGM strip, the AF detection algorithm may determine thatno arrhythmia episode is present or a false arrhythmia episode ispresent.

II. SYSTEM OVERVIEW

FIG. 1 illustrates an implantable cardiac monitoring device (ICM) 100intended for subcutaneous implantation at a site near the heart. The ICM100 includes a pair of spaced-apart sense electrodes 114, 126 positionedwith respect to a housing 102. The sense electrodes 114, 126 provide fordetection of far field electrogram signals. Numerous configurations ofelectrode arrangements are possible. For example, the electrode 114 maybe located on a distal end of the ICM 100, while the electrode 126 islocated on a proximal side of the ICM 100. Additionally, oralternatively, electrodes 126 may be located on opposite sides of theICM 100, opposite ends or elsewhere. The distal electrode 114 may beformed as part of the housing 102, for example, by coating all but aportion of the housing with a nonconductive material such that theuncoated portion forms the electrode 114. In this case, the electrode126 may be electrically isolated from the housing 114 electrode byplacing it on a component separate from the housing 102, such as theheader 120. Optionally, the header 120 may be formed as an integralportion of the housing 102. The header 120 includes an antenna 128 andthe electrode 126. The antenna 128 is configured to wirelesslycommunicate with an external device 154 in accordance with one or morepredetermined wireless protocols (e.g., Bluetooth, Bluetooth low energy,Wi-Fi, etc.). The housing 102 includes various other components such as:sense electronics for receiving signals from the electrodes, amicroprocessor for processing the signals in accordance with algorithms,such as the AF detection algorithm described herein, a loop memory fortemporary storage of CA data, a device memory for long-term storage ofCA data upon certain triggering events, such as AF detection, sensorsfor detecting patient activity and a battery for powering components.

In at least some embodiments, the ICM 100 is configured to be placedsubcutaneously utilizing a minimally invasive approach. Subcutaneouselectrodes are provided on the housing 102 to simplify the implantprocedure and eliminate a need for a transvenous lead system. Thesensing electrodes may be located on opposite sides of the device anddesigned to provide robust episode detection through consistent contactat a sensor—tissue interface. The ICM 100 may be configured to beactivated by the patient or automatically activated, in connection withrecording subcutaneous ECG signals.

The ICM 100 senses far field, subcutaneous CA signals, processes the CAsignals to detect arrhythmias and if an arrhythmia is detected,automatically records the CA signals in memory for subsequenttransmission to an external device 154. The CA signal processing and AFdetection is provided for, at least in part, by algorithms embodied inor implemented by the microprocessor. The ICM 100 includes one or moreprocessors and memory that stores program instructions directing theprocessors to implement AF detection utilizing an on-board R-R intervalirregularity (ORI) process that analyzes cardiac activity signalscollected over one or more sensing channels.

FIG. 2A shows a block diagram of the ICM 100 formed in accordance withembodiments herein. The ICM 100 may be implemented to monitorventricular activity alone, or both ventricular and atrial activitythrough sensing circuitry. The ICM 100 has a housing 102 to hold theelectronic/computing components. The housing 102 (which is oftenreferred to as the “can”, “case”, “encasing”, or “case electrode”) maybe programmably selected to act as an electrode for certain sensingmodes. Housing 102 further includes a connector (not shown) with atleast one terminal 113 and optionally additional terminals 115. Theterminals 113, 115 may be coupled to sensing electrodes that areprovided upon or immediately adjacent the housing 102. Optionally, morethan two terminals 113, 115 may be provided in order to support morethan two sensing electrodes, such as for a bipolar sensing scheme thatuses the housing 102 as a reference electrode. Additionally, oralternatively, the terminals 113, 115 may be connected to one or moreleads having one or more electrodes provided thereon, where theelectrodes are located in various locations about the heart. The typeand location of each electrode may vary.

The ICM 100 includes a programmable microcontroller 121 that controlsvarious operations of the ICM 100, including cardiac monitoring.Microcontroller 121 includes a microprocessor (or equivalent controlcircuitry), RAM and/or ROM memory, logic and timing circuitry, statemachine circuitry, and I/O circuitry. The microcontroller 121 alsoperforms the operations described herein in connection with collectingcardiac activity data and analyzing the cardiac activity data toidentify AF episodes.

A switch 127 is optionally provided to allow selection of differentelectrode configurations under the control of the microcontroller 121.The electrode configuration switch 127 may include multiple switches forconnecting the desired electrodes to the appropriate I/O circuits,thereby facilitating electrode programmability. The switch 127 iscontrolled by a control signal 128 from the microcontroller 121.Optionally, the switch 127 may be omitted and the I/O circuits directlyconnected to the housing electrode 114 and a second electrode 126.Microcontroller 121 includes an arrhythmia detector 134 that isconfigured to analyze cardiac activity signals to identify potential AFepisodes as well as other arrhythmias (e.g., Tachycardias, Bradycardias,Asystole, etc.). By way of example, the arrhythmia detector 134 mayimplement an AF detection algorithm as described in U.S. Pat. No.8,135,456, the complete subject matter of which is incorporated hereinby reference. Although not shown, the microcontroller 121 may furtherinclude other dedicated circuitry and/or firmware/software componentsthat assist in monitoring various conditions of the patient's heart andmanaging pacing therapies.

The ICM 100 is further equipped with a communication modem(modulator/demodulator) 140 to enable wireless communication. In oneimplementation, the communication modem 140 uses high frequencymodulation, for example using RF, Bluetooth or Bluetooth Low Energytelemetry protocols. The signals are transmitted in a high frequencyrange and will travel through the body tissue in fluids withoutstimulating the heart or being felt by the patient. The communicationmodem 140 may be implemented in hardware as part of the microcontroller121, or as software/firmware instructions programmed into and executedby the microcontroller 121. Alternatively, the modem 140 may resideseparately from the microcontroller as a standalone component. The modem140 facilitates data retrieval from a remote monitoring network. Themodem 140 enables timely and accurate data transfer directly from thepatient to an electronic device utilized by a physician.

The ICM 100 includes sensing circuitry 144 selectively coupled to one ormore electrodes that perform sensing operations, through the switch 127to detect cardiac activity data indicative of cardiac activity. Thesensing circuitry 144 may include dedicated sense amplifiers,multiplexed amplifiers, or shared amplifiers. It may further employ oneor more low power, precision amplifiers with programmable gain and/orautomatic gain control, bandpass filtering, and threshold detectioncircuit to selectively sense the features of interest. In oneembodiment, switch 127 may be used to determine the sensing polarity ofthe cardiac signal by selectively closing the appropriate switches.

The output of the sensing circuitry 144 is connected to themicrocontroller 121 which, in turn, determines when to store the cardiacactivity data of CA signals (digitized by the A/D data acquisitionsystem 150) in the memory 160. For example, the microcontroller 121 mayonly store the cardiac activity data (from the A/D data acquisitionsystem 150) in the memory 160 when a potential AF episode is detected.The sensing circuitry 144 receives a control signal 146 from themicrocontroller 121 for purposes of controlling the gain, threshold,polarization charge removal circuitry (not shown), and the timing of anyblocking circuitry (not shown) coupled to the inputs of the sensingcircuitry.

In the example of FIG. 2A, a single sensing circuit 144 is illustrated.Optionally, the ICM 100 may include multiple sensing circuits, similarto sensing circuit 144, where each sensing circuit is coupled to two ormore electrodes and controlled by the microcontroller 121 to senseelectrical activity detected at the corresponding two or moreelectrodes. The sensing circuit 144 may operate in a unipolar sensingconfiguration or in a bipolar sensing configuration. Optionally, thesensing circuit 144 may be removed entirely and the microcontroller 121perform the operations described herein based upon the CA signals fromthe A/D data acquisition system 150 directly coupled to the electrodes.

The arrhythmia detector 134 of the microcontroller 121 includes anon-board R-R interval irregularity (ORI) process 136 that detects AFepisodes using an automatic detection algorithm that monitors forirregular ventricular rhythms that are commonly known to occur duringAF. The ORI process 136 may be implemented as firmware, software and/orcircuits. The ORI process 136 uses a hidden Markov Chains and Euclidiandistance calculations of similarity to assess the transitionary behaviorof one R-wave (RR) interval to another and compare the patient's RRinterval transitions to the known RR interval transitions during AF andnon-AF episodes obtained from the same patient and/or many patients. TheORI process 136 detects AF episodes over a short number of RR intervals.For example, the ORI process 136 may implement the AF detection methodsdescribed in U.S. Pat. No. 8,135,456, the complete subject matter ofwhich is incorporated herein by reference in its entirety. As explainedherein, the ORI process 136 manages a sensitivity profile of the sensor144 during R-wave detection utilizing an automatic sensing control (ASC)adjustment to determine whether the CA signal has sufficient amplitudeto be analyzed for cardiac events. The ORI process 136 identifiesR-waves within the CA signals at points where the CA signal crosses thesensitivity profile (outside of a refractory period). The ORI process136 tracks RR intervals within the CA signal and identifies AF eventswithin the CA signal based on irregularities in the RR interval. When asufficient number (e.g., X cardiac events out of Y cardiac events) ofthe cardiac events within the CA signal are identified as AF events, theORI process 136 declares an AF episode.

Optionally, the microcontroller 121 may also include a confirmatoryfeature detection process 137 configured to implement one or more of theoperations discussed herein, such as all or a portion of the enhancedconfirmatory AF detection process of FIG. 3 and/or all or a portion ofthe AF detection classifying and recommendation process of FIG. 4. As afurther example, the confirmatory feature detection process 137 mayimplement one or more of the R-wave detection processes, noise detectionprocesses, P-wave detection processes and PVC detection processesdescribed in the Co-Pending Related Applications.

FIG. 2B illustrates an automatic sensing control adjustment utilized bythe ORI process 136 of the ICM 100 in accordance with embodimentsherein. FIG. 2B illustrates an example cardiac activity signal 151 afterpassing through a rectifier to convert all positive and negativedeflections within the cardiac activity signal 151 to be positivedeflections. The ORI process 136 manages the sensor 144 to have asensitivity profile 163 (denoted by a dashed line) that varies overtime.

In a basic implementation, the ORI process 136 utilizes a conventionalautomatic sensing control adjustment based on a conventional sensitivityprofile 153. The sensitivity profile 153 is defined by sensitivityprofile parameter settings corresponding to the threshold startsensitivity 161, decay delay time interval 169, maximum sensitivity 157and slope of the sensitivity decay 165. Optionally, the sensitivitydecay 165 may be defined in accordance with a non-linear monotonicallychanging shape from the threshold start sensitivity 161 to the maximumsensitivity 157. The start sensitivity parameter defines a startsensitivity of the sensitivity profile. For example, the startsensitivity parameter may set a start sensitivity to a percentage of thepreceding R-wave peak amplitude. The refractory period/interval durationparameter defines a blanking interval beginning at a sensed R-wave,during which the processors do not search for a T-wave. The decay delayparameter defines the interval at which the sensitivity profilemaintains the sensitivity level at a constant level following expirationof the refractory period before the sensitivity profile beginsdecreasing. When the sensitivity profile includes a linear sensitivitylevel decline, the decay delay rate defines a slope of the linearsensitivity level decline. The maximum sensitivity limit defines alowest sensitivity level (e.g., maximum resolution) that linearsensitivity decline is allowed to reach. The sensitivity parameters arepreprogrammed to fixed values and, over the operation of the IMD, areonly modified (if at all) by a clinician.

In accordance with the sensitivity profile 153, when the CA signal 151crosses the sensitivity profile 153 at starting point 155, the ORIprocess 136 treats the point 155 as a sensed R-wave and begins arefractory interval 159. No new R-wave (or T-wave) will be sensed duringthe refractory interval 159. At the end of the refractory interval 159,the sensitivity is adjusted to a threshold start sensitivity 161. Thethreshold start sensitivity 161 is defined as a percentage of the peakamplitude 163 of the QRS complex of the CA signal 151 detected duringthe refractory interval 159. The sensing circuit 144 maintains thethreshold start sensitivity 161 for a decay delay time interval 169,after which the ORI process 136 begins to monotonically decrease thesensitivity (increase the resolution) of the sensing circuit 144 asdenoted by the sensitivity decay 165 within the sensitivity profile 153.The sensing circuit 144 continues to decrease the sensitivity untileither the sensitivity decay 165 reaches the maximum sensitivity 157 oran amplitude of the rectified cardiac activity signal 151 exceeds thesensor sensitivity profile 153, such as at a point 167 where a newsensed R wave is detected.

The sensitivity of the sensing circuit 144 (FIG. 2A) is continuouslyadjusted by the microcontroller 121 in accordance with the sensitivityprofile 153 over the course of an individual cardiac event. However, theconventional ORI process does not modify the parameter settings of thesensitivity profile beat by beat or on demand sensitivity profileparameter

In accordance with embodiments herein, the values of the sensitivityparameters may be adjusted based on whether the ORI process 136 isdeemed to declare false AF detection R-waves. False AF detection mayoccur in connection with inappropriate R-wave sensing which may arisefrom under-sensing of R-waves and/or over-sensing of non-R-waves (e.g.,noise, or P-waves, or T-waves as R-waves). For example, the confirmatoryfeature detection process 137 may determine when the ORI process 136declares an undesirable number of false AF detections and in responsethereto adjust one or more sensitivity profile parameters. Additionally,or alternatively, the confirmatory feature detection process may beimplemented external to the ICM 100, such as at a local external deviceor remote server. The local external device and/or remote server maythen return, to the ICM 100, adjustments to the sensitivity profileparameters when an externally implemented confirmatory feature detectionprocess identifies an undesirable number of false AF detections.

Returning to FIG. 2A, the ICM 100 further includes an analog-to-digitalA/D data acquisition system (DAS) 150 coupled to one or more electrodesvia the switch 127 to sample cardiac activity signals across any pair ofdesired electrodes. The data acquisition system 150 is configured toacquire cardiac electrogram (EGM) signals as CA signals, convert the rawanalog data into digital data, and store the digital data as CA data forlater processing and/or telemetric transmission to an external device154 (e.g., a programmer, local transceiver, or a diagnostic systemanalyzer). The data acquisition system 150 is controlled by a controlsignal 156 from the microcontroller 121. The EGM signals may be utilizedas the cardiac activity data that is analyzed for potential AF episodes.The ACS adjustment and ORI process 136 may be applied to signals fromthe sensor circuit 144 and/or the DAS 150.

By way of example, the external device 154 may represent a bedsidemonitor installed in a patient's home and utilized to communicate withthe ICM 100 while the patient is at home, in bed or asleep. The externaldevice 154 may be a programmer used in the clinic to interrogate the ICM100, retrieve data and program detection criteria and other features.The external device 154 may be a handheld device (e.g., smartphone,tablet device, laptop computer, smartwatch and the like) that can becoupled over a network (e.g., the Internet) to a remote monitoringservice, medical network and the like. The external device 154facilitates access by physicians to patient data as well as permittingthe physician to review real-time CA signals while collected by the ICM100.

The microcontroller 121 is coupled to a memory 160 by a suitabledata/address bus 162. The programmable operating parameters used by themicrocontroller 121 are stored in memory 160 and used to customize theoperation of the ICM 100 to suit the needs of a particular patient. Suchoperating parameters define, for example, detection rate thresholds,sensitivity, automatic features, AF detection criteria, activity sensingor other physiological sensors, and electrode polarity, etc.

In addition, the memory 160 stores the cardiac activity data, as well asthe markers and other data content associated with detection ofarrhythmia episodes. The operating parameters of the ICM 100 may benon-invasively programmed into the memory 160 through a telemetrycircuit 164 in telemetric communication via communication link 166 withthe external device 154. The telemetry circuit 164 allows intracardiacelectrograms and status information relating to the operation of the ICM100 (as contained in the microcontroller 121 or memory 160) to be sentto the external device 154 through the established communication link166. In accordance with embodiments herein, the telemetry circuit 164conveys the cardiac activity data, markers and other information relatedto AF episodes.

The ICM 100 may further include magnet detection circuitry (not shown),coupled to the microcontroller 121, to detect when a magnet is placedover the unit. A magnet may be used by a clinician to perform varioustest functions of the housing 102 and/or to signal the microcontroller121 that the external device 154 is in place to receive or transmit datato the microcontroller 121 through the telemetry circuits 164.

The ICM 100 can further include one or more physiologic sensors 170.Such sensors are commonly referred to (in the pacemaker arts) as“rate-responsive” or “exercise” sensors. The physiological sensor 170may further be used to detect changes in the physiological condition ofthe heart, or diurnal changes in activity (e.g., detecting sleep andwake states). Signals generated by the physiological sensors 170 arepassed to the microcontroller 121 for analysis and optional storage inthe memory 160 in connection with the cardiac activity data, markers,episode information and the like. While shown as being included withinthe housing 102, the physiologic sensor(s) 170 may be external to thehousing 102, yet still be implanted within or carried by the patient.Examples of physiologic sensors might include sensors that, for example,activity, temperature, sense respiration rate, pH of blood, ventriculargradient, activity, position/posture, minute ventilation (MV), and soforth.

A battery 172 provides operating power to all of the components in theICM 100. The battery 172 is capable of operating at low current drainsfor long periods of time. The battery 172 also desirably has apredictable discharge characteristic so that elective replacement timecan be detected. As one example, the housing 102 employs lithium/silvervanadium oxide batteries. The batten 172 may afford various periods oflongevity (e.g., three years or more of device monitoring). In alternateembodiments, the battery 172 could be rechargeable. See for example,U.S. Pat. No. 7,294,108, Cardiac event micro-recorder and method forimplanting same, which is hereby incorporated by reference.

The ICM 100 provides a simple to configure data storage option to enablephysicians to prioritize data based on individual patient conditions, tocapture significant events and reduce risk that unexpected events aremissed. The ICM 100 may be programmable for pre- and post-trigger eventstorage. For example, the ICM 100 may be automatically activated tostore 10-120 seconds of CA data prior to an event of interest and/or tostore 10-120 seconds of post CA data. Optionally, the ICM 100 may affordpatient triggered activation in which pre-event CA data is stored, aswell as post event CA data (e.g., pre-event storage of 1-15 minutes andpost-event storage of 1-15 minutes). Optionally, the ICM 100 may affordmanual (patient triggered) or automatic activation for CA data.Optionally, the ICM 100 may afford additional programming options (e.g.,asystole duration, bradycardia rate, tachycardia rate, tachycardia cyclecount). The amount of CA data storage may vary based upon the size ofthe memory 160.

The ICM 100 may provide comprehensive safe diagnostic data reportsincluding a summary of heart rate, in order to assist physicians indiagnosis and treatment of patient conditions. By way of example,reports may include episodal diagnostics for auto trigger events,episode duration, episode count, episode date/time stamp and heart ratehistograms. The ICM 100 may be configured to be relatively small (e.g.,between 2-10 cc in volume) which may, among other things, reduce risk ofinfection during implant procedure, afford the use of a small incision,afford the use of a smaller subcutaneous pocket and the like. The smallfootprint may also reduce implant time and introduce less change in bodyimage for patients.

FIG. 2C illustrates cardiac activity data generated and stored by theICM 100 in memory 160 in accordance with embodiments herein. The CA data141 is stored by the ICM in response to detection of episodes ofinterest, patient initiated instructions, physician initiatedinstructions and the like. The CA data 141 may include, among otherthings, patient and ICM identification information 142. By way ofexample, the patient identification information may include a patientunique medical record number or other identifier, patient name and/orpatient demographic information. The ICM ID may include a serial numberor other unique identifier of the ICM, software and firmware versionnumbers, and/or a unique wireless ID. The CA data 141 includes one ormore signal channels 143 that store CA signals collected by acorresponding sensing channel (e.g., sensor circuit 144 or DAS 150). TheCA signal channel 143 may include EGM signals for a series of cardiacbeats/events sensed by the ICM. The CA data 141 also includes a markerchannel 145 having, among other things, device documented markersidentified by the ICM 100 in connection with the CA signal. The devicedocumented markers within the marker channel 145 may include devicedocumented markers indicative of normal sinus features, AF detectedevents, AF detected episodes and the like. For example, the ORI process136 (FIG. 2A) utilizes the sensitivity profile 153 (FIG. 2B) to identifyR-waves in the CA signal.

The content of the CA signal channel 143 and marker channel 145 may bedisplayed on a display of an external device (e.g., smart phone, tabletdevice, computer, smart watch, etc.) as corresponding types of CA andmarker waveforms (e.g., in a rhythm display screen). In the presentexample, a single CA signal channel 143 is described in connection witha single CA signal. Optionally, embodiments herein may be implemented inconnection with multiple CA signal channels. For example, the ICM 100may be configured to include multiple sensing channels with differentsensing characteristics. As one example, a first sensing channel may beconfigured to perform full range signal sensing, such as in connectionwith detecting R-waves (corresponding to the CA signal channel 143). Asecond sensing channel may be configured to perform narrow range signalsensing, such as in connection with detecting P-waves which have muchsmaller amplitude in comparison to the R-waves. Optionally, multiple ECGsignals may be displayed in parallel and temporally aligned with EGM andmarker waveforms.

The CA data 141 also includes episode statistics 147 and arrhythmiadiagnostics 149. The episode statistics 147 may be presented in a windowon a user interface to list various statistical data for any or allepisodes recorded by the ICM 100 since the episode and CA data storagewere last cleared. Optionally, the episode statistics 147 may also listthe number of inhibited VT diagnoses due to arrhythmia qualifiers, suchas a bigeminal rhythm qualifier, and/or other rhythm discriminators. Asfurther nonlimiting examples, the episode statistics 147 may alsoinclude a date of a last programmer session, date of the last ICMinterrogation, the date of the presently stored episodes and the datewhen EGMs were last cleared from the ICM and the like.

FIGS. 2D and 2E illustrate screenshots of displays in which episodestatistics and arrhythmia diagnostics may be presented to a physician inaccordance with an embodiment herein. The arrhythmia diagnostics 149 mayrepresent cumulative diagnostic information for a period of time, suchas when the diagnostics data is last cleared from the ICM. Thearrhythmia diagnostics 149 may include various information concerningheart rate, such as ventricular heart rate histograms, dates and timesof last programmer sessions, diagnostic data last read, diagnostic datalast cleared and the like. The arrhythmia diagnostics 149 may alsoinclude AF diagnostics, such as AF burden 149A, AF summaries, AFstatistical data 149B, dates and times of last programmer session, lasttime the AF diagnostic data were read, last time the AF diagnostic datawas cleared and the like. By way of example, AF burden may be displayedin an AF diagnostics window of a computing device formatted as one ormore bar graphs of a percentage of time (as shown in FIG. 2E) that thepatient experienced AF during a predetermined period of time (e.g., eachday, each week, each month). The AF burden may show a percentage of timethat the patient was in AF since the AF diagnostics data were lastcleared. The AF summary may include one or more graphs of meanventricular heart rate and a duration of AF episodes since the AFdiagnostic data were last cleared. The AF diagnostic data may accruevarious cumulative totals concerning AF episodes detected and/or storedsince the AF diagnostic data were last cleared. The AF statistics mayinclude, among other things, a total number of AF episodes, AF burdentrends, AF episode duration histograms, mean ventricular rate during AFand the like.

As explained herein, an enhanced confirmatory AF detection process isimplemented to analyze the results of the baseline analysis performed bythe ORI process in the ICM. The enhanced confirmatory AF detectionprocess determines whether AF episodes declared by the ICM are true orfalse, and updates the AF diagnostics in connection there with. Next,various processes are described in connection with embodiments hereinthat are performed by one or more of the circuits, processors and otherstructures illustrated in the figures and described in thespecification.

FIG. 3 shows a high-level workflow for an enhanced confirmatory AFdetection process implemented in accordance with embodiments herein. Byway of example, the operations of FIG. 3 may be implemented, as aconfirmatory process, where cardiac activity signals have beenpreviously analyzed by an AF detection module, such as the ORI processdescribed in connection with FIGS. 2A and 2B. The process may initiatethe operations of FIG. 3 in an attempt to verify whether one or moreepisodes in a CA data set, are in fact an AF episode or a normalrhythmic/sinus episode. Optionally, the operations of FIG. 3 may beimplemented in connection with a CA data set that has not beenpreviously analyzed for potential AF episodes. The operations of FIG. 3may be implemented as part of a local or distributed system, such as bythe microcontroller 121 of the ICM, by a local external device and/or aremote server.

At 302, one or more processors of the system obtain a cardiac activity(CA) data set including CA signals recorded in connection with a seriesof cardiac events. The CA data includes device documented arrhythmicmarkers including identifying AF entry and/or exit within the series ofcardiac events. The CA data also includes device documented rhythmicmarkers (e.g., R-wave) to identify the cardiac beats sensed by thedevice within the series of cardiac events. The CA data also includedevice documented activity and noise markers to identify periods of timeunder significant physical activity and/or noise interrupt within theseries of cardiac events. All device documented markers are declared anddesignated by the ICM utilizing the ORI process to analyze the CAsignals.

For example, the cardiac activity data may be obtained by an externalmonitoring device or ICM that includes electrodes that sense CA signals,such as electrocardiogram (ECG) signals and/or intra-electrocardiogram(EGM) signals. The ECG and/or EGM signals may be collected by asubcutaneous ICM that does not include a transvenous lead or otherwiseexperiences difficulty in sensing P-waves and/or R-waves. The cardiacactivity data may have been previously acquired and stored in memory ofan implantable or external monitoring device, implantable or externaltherapy delivery device, programmer, workstation, healthcare network orother system. When the cardiac activity data has been previouslyacquired, the obtaining operation at 302 represents accessing andreading the previously stored cardiac activity data.

The operations of FIG. 3 may be staged to be performed upon the CA dataat various times, such as in real time (e.g., during or shortly after apatient experiences an episode) or at any time after storage of the CAdata. The operations of FIG. 3 may be performed by devices and systemsat various proximity to a patient with the ICM. For example, the CA datamay be read out of an ICM and transmitted to a local portable externaldevice (e.g., smartphone, table computer, laptop computer, smartwatch,etc.), where the local portable external device locally implements allor a portion of the operations described in connection with FIG. 3 whilein close proximity to the patient. Additionally, or alternatively, theCA data may be read out of the ICM to a local portable external deviceand transmitted to a remote server, medical network, physician computerand the like, which implements all or a portion of the operationsdescribed in connection with FIG. 3 remote from the patient.Additionally, or alternatively, the CA data may be read from the ICM bya programmer device, such as during a patient visit to a physician,where the programmer device implements all or a portion of theoperations described in connection with FIG. 3 during or after apatient-doctor visit.

The CA data may include CA signals for a series of cardiac eventsspanning over various periods of time. As one example, one segment orset of the cardiac activity data may be collected for an interval thatis 30 seconds to 5 minutes in length and that includes one or more ICMdeclared AF episodes. As another example, one segment or set of thecardiac activity data may be collected for an interval that begins 10-60seconds before an episode of interest (e.g., an AF episode) and thatends 10-60 seconds after the episode of interest. A CA data set mayinclude one or multiple AF episodes. The duration of a CA data set maybe programmed for a predetermined period of time based on detection ofAF episodes and/or based on other criteria. The predetermined period oftime may be programmed by a clinician, or automatically updated by oneor more processors throughout operation. By way of example, thepredetermined period of time may correspond to one minute, 30 minutes,one hour or otherwise. The CA data obtained at 302 may correspond to onedetected AF episode and/or multiple detected AF episodes. The CA dataset obtained at 302 may correspond to one continuous series of cardiacevents (e.g., 1 continuous series for 30 seconds to 5 minutes) and/orseparate sets of cardiac events (3-10 separate series, each for 30seconds to 3 minutes of cardiac events).

Collection and analysis of CA signals by the ICM may be initiatedautomatically when the ICM detects an episode of interest. Additionally,or alternatively, the ICM may collect and analyze CA signals in responseto a user-initiated instruction. For example, a user may utilize a smartphone or other portable device to establish a communications sessionwith the ICM and instruct the ICM to begin to collect and analyzecardiac signals, such as when the patient is experiencing discomfort,feeling faint, a rapid heart rate, etc.

At 304 to 320, the one or more processors determine whether the on-boardRR interval irregularity process (implemented by the ICM_declared one ormore false positive AF episodes, such as due to under-sensing orover-sensing features within the CA signal. The operations at 304 to 320generally perform an R-wave enhancement and feature rejection (EFR)process. The EFR process enlarges or exaggerates features of interest(e.g., R-wave) within the CA signal and optionally suppresses at leastcertain features not of interest (e.g., non-R-wave features such asnoise, T-waves) to obtain confirmatory feature markers. The EFR processapplies a series of tests to confirm or reject alternative conditionsthat a patient may have experienced. The operations at 306 to 320confirm or reject a presence or absence of certain rhythmic, physiologicand non-physiologic (e.g., noise) features within the CA data.Non-limiting examples of the features, for which the process searchesinclude noise, R-wave changes, P-waves, and post ventricularcontractions.

At 304, the one or more processors analyze the CA data for noise andpass or remove segments of the CA signal for select cardiac events basedon a noise level within the corresponding segment of the CA signal. Thenoise is identified based on noise discrimination parameters that areset to a desired sensitivity level. While the sensitivity of the noisedetection process at 304 may be adjusted, the sensitivity of the noisedetection process at 304 is more selective than the on-board noisedetection circuit in the ICM. For example, at 304, the one or moreprocessors may implement the noise detection process described in one ormore of the Co-Pending Related Applications referred to above, filedconcurrently on the same day as the present application. For example,the operation at 304 generally represents a software based evaluation ofthe CA data to detect noise. The software based evaluation can bedeveloped in a manner that is tailored to AF detection such that thesoftware-based noise rejection is mare sensitive in connection withidentifying or removing unduly noisy CA signal segments that in turngive rise to inappropriate R-wave detection, leading to false AFepisodes declaration by the ICM. The original CA data processed inconnection with FIG. 3 results from the onboard ORI process of the ICM.The onboard ORI process processes incoming signals that have firstpassed through a hardware-based noise detect that applies noisediscrimination the hardware-based noise detector is not as sensitive as,and not as adaptable as, the software based noise discriminationimplemented at 304. Also, depending upon a complexity of thesoftware-based noise discrimination, processors of an ICM may not have asufficient processing power to implement the software noisediscrimination. The extent to which the software-based noisediscrimination may be implemented on an ICM depends in part upon thesensitivity level desired. For example, the discrimination parametersmay be set to a very “conservative” level such that the noise detectoronly eliminates CA signals for cardiac events that include a substantialamount of noise (e.g., the signal to noise ratio is less than or equalto 50%). Levels for the noise discrimination parameters may be adjustedto eliminate more cardiac events that include relatively intermediatelevels of noise (e.g., the signal to noise ratio is between 75% and90%). The noise discriminator passes CA signals for cardiac events thathave less noise than the level defined by the noise discriminationparameters.

Optionally, at 304, when the noise level is sufficiently high (e.g.,satisfying a threshold), the initial AF diagnosis/declaration by the ICMmay be overridden. For example, when the noise level exceeds a thresholdin connection with an AF episode declared by the ICM, the processors maycancel the AF episode declaration and reset any counters set inconnection there with.

At 306, the one or more processors apply a feature enhancement processto form modified CA signals in which sinus features of interest areenlarged or exaggerated relative to the original/baseline CA signals.Optionally, at least certain features not of interest (e.g., noise,T-waves) are reduced or suppressed relative to the baseline CA signalsin order to generate the confirmatory feature (e.g., R-wave) marker. Forexample, at 306, the one or more processors may implement the featureenhancement process described in one or more of the Co-Pending RelatedApplications referred to above, filed concurrently on the same day asthe present application.

At 307, the one or more processors analyze the modified CA signalutilizing a confirmatory feature detection process. For example, at 306,the one or more processors may implement, as the confirmatory featuredetection process, the R-wave detection processes described in one ormore of the Co-Pending Related Applications referred to above, and filedconcurrently on the same day as the present application. The processorsanalyze the modified CA signal to identify R-waves, and store a set ofconfirmatory feature markers separate and distinct from the devicedocumented (DD) feature markers.

At 308, the one or more processors determine whether the confirmatoryfeature markers match or differ from the DD feature markers. Forexample, the determination at 308 may be based on a simple count of thenumber of DD feature markers as compared to a count of the number ofconfirmatory feature markers. Additionally, or alternatively, thedetermination at 308 may determine whether the confirmatory featuredetection process identified confirmatory feature markers (e.g.,R-waves) from the CA signals that were not identified by the ORI processor displaced significantly. For example, the DD and confirmatory featuremarkers for the CA data may be aligned temporally and compared toidentify differences.

Differences may occur due to various reasons. For example, the ORIprocess may under-sense R-waves, while the confirmatory featuredetection process properly identifies a feature of interest in themodified CA signal as an R-wave. As another example, the ORI process mayover sense R-waves, while the confirmatory feature detection processproperly determines that no R-wave is present in a particular segment ofthe CA signal. Additionally, or alternatively, a difference may bedeclared when the ORI process and confirmatory feature detection processboth declare an R-wave for a specific cardiac event, but the DD andconfirmatory R-waves are temporally offset from one another in time bymore than a desired R-wave offset threshold.

When the process determines at 308 that a difference or change existsbetween the confirmatory and DD feature markers, flow moves to 310. Whenthe process determines that no difference or change exists between theconfirmatory and DD feature markers, flow moves to 312. At 310 the oneor more processors identify instability in the confirmatory featuremarkers. At 310, the one or more processors determine whether theinstability within the confirmatory feature marker indicates AF. Theprocessors determine the presence or absence of instability by analyzingvariation in the RR intervals between the confirmatory features markers,such as using the processors described in the Co-Pending RelatedApplication and/or the '456 patent. If the instability/variation equalsor is below a stability threshold, the segment of the CA signal isconsidered to exhibit a stable feature-to-feature interval that does notindicate AF. Consequently, flow moves to 316. Alternatively, when theinstability is above the instability threshold, the analysis of the CAsignal segment is considered to exhibit an unstable feature-to-featureinterval. Consequently, flow moves to 312.

At 316, when AF is not indicated, the one or more processors classify anepisode in the CA data set to be a DD false positive or false detection.At 316, the one or more processors may perform additional operations,such as setting one or more flags to track the declaration of DD falsepositives by the ORI process on the ICM. Additionally, or alternatively,at 316, the one or more processors may reverse a diagnosis of AF, adjustvarious statistics tracking the patient's behavior and the like. Forexample, the AF diagnostics (e.g., 149 in FIG. 2C) may be updated tocorrect for false AF detection. Additionally, or alternatively, a memorysegment within the ICM that includes the CA data set associated with afalse AF detection may be set to have a lower priority. Reassignment ofpriority levels to different memory segments may be utilized inconnection with overwriting memory segments during future use. Forexample, when the CA data memory of the ICM approaches or becomes full,the memory segment assigned the lowest priority may then be overwrittenfirst when the ICM detects new AF episodes.

When flow advances to 312, the potential still exists that the CAsignals does not include an AF episode. Therefore, the process of FIG. 3performs additional analysis upon the CA data. At 312, the one or moreprocessors perform a P-wave detection operation to determine whetherP-waves are present within the CA signal segment being analyzed. Forexample, at 312, the one or more processors may implement the P-wavedetection process described in one or more of the Co-Pending RelatedApplications referred to above, and filed concurrently on the same dayas the present application. When a P-wave is identified to be present inthe CA signal, the process determines that the presence of a P-waveindicates that the current episode is not an AF episode even though RRinterval irregularity may be present. Accordingly, flow moves to 316.

Alternatively, at 312 when the one or more processors determine that noP-waves are present within the CA signal, a potential still remains thatthe CA signal does not correspond to an AF episode. Accordingly, flowadvances to 318 where additional analysis is applied to the CA data set.At 318, the one or more processors apply a morphology based prematureventricular contraction (PVC) detection operation. For example, at 318,the one or more processors may implement the QRS complex morphologybased PVC detection process described in one or more of the Co-PendingRelated Applications referred to above, and filed concurrently on thesame day as the present application. The processors determine whether aQRS complex morphology has varied beyond a morphology variationthreshold. Variation in the R-wave morphology beyond the morphologyvariation threshold provides a good indicator that the cardiac eventsinclude one or more PVC. When the cardiac events include a sufficientnumber of PVCs, the process may attribute an R-R interval variation to(and indicative of) PVCs or non-atrial originated beats that lead tosignificantly different R-R intervals, and not due to (or indicative of)an AF episode. Accordingly, when the R-wave morphology exceeds themorphology variation threshold, flow returns to 316, where the processperforms the operations described herein. At 316, one or more flags maybe set to indicate that the false AF detection was declared due to oneor more PVCs present within the CA data. Additionally, or alternatively,a diagnosis may be changed from AF episode to PVC episode. The number ofPVC may vary that are needed to achieve an R-wave morphology variationat 318 sufficient for flow to branch to 316 (e.g., declare a false AFdetection).

At 318, alternatively, when the R-wave morphology does not exceed themorphology variation threshold, the process interprets the condition asan indicator that the cardiac events do not include significant numberof PVCs. Thus, flow moves to 320. At 320, the one or more processorsconfirm a device documented AF episode and records the current episodeto remain as originally declared by the ORI process.

Optionally, the sequence of operations discussed in connection with FIG.3 may be changed and/or some of the operations may be omitted dependingon computational and performance objectives. For example, it may bedetermined that a low probability exists that a particular patient (orICM) experiences PVCs that cause false AF detection, and thus, theprocess of FIG. 3 may omit the PVC detection operation at 318.Additionally, or alternatively, it may be determined that a lowprobability exists that an ICM is incorrectly detecting P-waves asR-waves that would cause false AF detection, and thus, the process ofFIG. 3 may omit the P-wave detection operation at 312.

Additionally, or alternatively, it may be determined that lessprocessing time/power is utilized to identify P-waves (operations at312) and/or PVCs (operations at 318) that cause false AF detection, ascompared to R-wave detection and analysis of RR interval stability(operations at 306 310). Accordingly, the P-wave and/or PVC detectionoperations may be performed before the R-wave detection and analysis. Inthe present example, in the event a P-wave or PVC is detected, theprocess may declare a CA data set to include a false AF detectionwithout performing the further computations for R-wave detection andanalysis.

Optionally, the operations at 308-318 may be modified to not representbinary branches between alternative paths. Instead, the decisions atoperations 308-318 may result in a score or a vote, rather than a binary“AF” or “not AF”. The vote or score may be variable based upon a degreeto which the feature of interest in the confirmatory analysis matchesthe determination from the original ORI process. Additionally, oralternatively, the vote or score may be based on a degree to which thefeature of interest from the confirmatory analysis matches one or morebaseline values. The votes or scores may be used in conjunction withother AF detection algorithms in order to find a probability that an AFepisode has occurred.

The operations of FIG. 3 may be repeated periodically or in response todetection of particular criteria, such as detection of potential atrialfibrillation episodes or otherwise.

The operations of FIG. 3 afford a powerful, sophisticated process toconfirm AF detection within ECG and EGM signals in a non-real timemanner. The AF detection confirmation processes described herein mayutilize computationally expensive analysis that may otherwise not be tobe implemented in an on-board circuit within an ICM, either due tomemory and power constraints, processing power constraints, and/or aninability to complete the analysis in real time.

Optionally, the operations of one or more of the stages within theprocess of FIG. 3 may be adapted to run in ICM firmware, althoughfirmware implementations may exhibit different overall performance. In afirmware implementation, a similar form of step-by-step discriminationon existing AF episodes may be achieved. Alternatively, some or all ofthe features may be adapted for real-time use and set as additional oralternative signals. For example, the determinations at 306-318 mayproduce factors that are applied to an AF probability and sudden onsetdetermination as AF detection criteria.

FIG. 4 illustrates a flow chart for classifying AF detection anddeveloping recommendations for sensitivity profile parameter settings inaccordance with embodiments herein. For example, the operations of FIG.4 may be performed at 316 and/or 320 in FIG. 3 and/or at other points inthe processes described herein. The operations of FIG. 4 build and/oradd to a confirmation log that tracks and records the differences andsimilarities between the results of the EFR and ORI processes. Theconfirmation log may be stored together with, or separate from, theunderlying baseline CA data set and/or the modified CA data set.Optionally, the confirmation log may not represent a separate file, butinstead merely represent parameter settings or other informationappended to the original or modified CA data set. For example, theconfirmation log may be saved as metadata or otherwise appended to theCA data set.

At 402, the one or more processors of the system determine whether theEFR process identified one or more false AF detection by the ORI processapplied by the ICM. When the EFR process and the ORI process detect acommon or similar number/degree of AF episodes in the CA data set, flowmoves to 404. At 404, the one or more processors record a match betweenthe results of the EFR and ORI processes. The match is stored in theconfirmation log. When the EFR process identifies a false AF detectionthat was declared by the ORI process, flow moves to 406.

At 406, the one or more processors classify the false AF detection intoone of multiple different categories. Non-limiting examples of thecategories include noise, inappropriate sensing, irregular sinus rhythm,frequent PVCs and the like. The processors may classify the false AFdetection as noise when the baseline CA data set is determined to havean excessive amount of noise (at 302). For example, the excessive amountof noise may be determined when a number of cardiac events that areremoved/suppressed (at 304, 312, 318) exceeds a threshold and/or exceedsa percentage of the total number of cardiac events in the CA data set.The processors may classify the false AF detection as inappropriatesensing when the feature detection (at 306) determines that the CA dataincludes more or few features of interest (e.g., under-sensed R-waves orover-sensed false R-waves). The processors may classify the false AFdetection as sinus rhythm when the P-wave detection (at 312) determinesthat the CA data set includes one or more P-waves. The processors mayclassify the false AF detection as frequent PVCs when the PVC detection(at 318) determines that the CA data exceeds a PVC threshold.

At 408, the one or more processors record the classification identifiedat 406 in the confirmation log. At 410, the one or more processorsdetermine whether additional guidance is to be provided for settingsensitivity profile parameters of the ICM. For example, the processors,at 410, may determine whether an extent or degree of the false R-waveand AF detection (e.g., number of under-sensed R-waves, number ofP-waves (as well as T-wave or noise artifact) classified as R-waves,number of frequent PVCs) exceeds a threshold that justifies adjustingone or more sensitivity profile parameters of the ICM. When sensitivityprofile parameter adjustments can be made, flow moves to 412. Otherwise,flow continues to 414.

When the extent or degree of the false R-wave and AF detection warrantsa parameter adjustment, the sensitivity profile parameter adjustment isdetermined based in part on the classification at 406. At 412, the oneor more processors declare an adjustment to the sensing parameters basedon a nature and/or extent of the false R-wave and AF detection. Forexample, when a false AF detection is classified as due to inappropriatesensing, the processors may declare the sensitivity profile parameteradjustment to be an increase or decrease in the feature (e.g., R-wave)detection threshold. As another example, the processors may declare thesensitivity profile parameter adjustment to be an increase in the R-wavedetection threshold when P-waves are identified as R-waves by the ORIprocess. As another example, the processors may declare the sensitivityprofile parameter adjustment to be an increase in the decay delay valuewhen the ORI process over senses T-waves and designates the T-waves tobe R-waves. The sensitivity profile parameter adjustment is saved in theconfirmation log. Optionally, the confirmation log may also maintain aPVC count.

The increase or decrease in the sensitivity profile parameter adjustmentmay be a predefined step (e.g., increase threshold by X mV or Y %).Optionally, the increase or decrease may be based on an extent or natureof the false R-wave and AF detection. For example, when the ORI processunder-sensed multiple R-waves in the CA data set, the process maydecrease the R-wave detection threshold by a larger factor as comparedto when the ORI process under-senses one or a few R-waves out ofmultiple R-waves. As another example, a decay delay value adjustmentand/or refractory period value adjustment may be determined based inpart on a number of T waves sensed as R-waves, a timing between the Twaves and corresponding preceding R-waves, and/or a peak amplitude ofthe T waves relative to the sensing sensitivity at the time the T-waveis detected.

Optionally, the one or more processors may identify additional oralternative sensitivity profile parameter adjustments based on adatabase of sensitivity profile parameter settings that are correlatedto cardiac activity data for a patient population. For example, adatabase may be maintained of EGM or ECG data segments collected inconnection with numerous patients that experienced AF, sinus rhythmsand/or other arrhythmias, where the EGM/ECG data segments are correlatedwith sensitivity profile parameter settings that are used by amonitoring device to collect the EGM or ECG data. The patient populationdatabase may also indicate which sensitivity profile parameter settingsachieved desired results and which sensitivity profile parametersettings did not achieve desired results. The database may furtherinclude quality indicators indicative of whether the sensitivity profileparameter settings were deemed to collect good or accurate results(e.g., correctly sense R-waves without over-sensing P-waves or T waves,and correctly sense all R-waves without under-sensing of R-waves withsmaller amplitude). The database may further include quality indicatorsindicative of whether the sensitivity profile parameter settings weredeemed to accurately declare AF detection in a high percentage of theinstances of AF. The quality indicators may be automatically enteredbased on automated analysis of the data within the database and/orentered by physicians or other medical personnel as sensitivity profileparameter settings are adjusted for individual patients. The databasemay be available on a medical network, through a cloud computing serviceand/or other local or remote source.

At 414, the one or more processors compare the current false AFdetection, modified CA data set and/or baseline CA data to a database ofthird-party CA data sets and false/valid AF detections for otherpatients. The processors may identify matches or similarities betweenthe false/valid AF detection, modified CA data set and/or baseline CAdata set, for the current patient, and the corresponding type of AFdetections and third-party CA data set from the database of the largerpopulation. When no match occurs, the operations of FIG. 4 end.Alternatively, when one or more matches occur between the current CAdata set and the patient population database, flow moves to 416. At 416,the one or more processors identify additional or alternativesensitivity profile parameter adjustments to record in the confirmationlog for the present patient based on the matches or similar cases fromthe database and the present patient.

The sensitivity profile parameter adjustments, in the confirmation log,may be presented on a display of a mobile device, computer, workstation,etc., as a suggestion or option ICM for the physician or other medicalpersonnel to apply to a current. Optionally, the sensitivity profileparameter adjustments may be pushed and uploaded to the ICM from a localportable external device and/or a remote medical network. Thesensitivity profile parameter adjustments may be pushed to the ICM atthe direction of the physician or other medical personnel, after thephysician or medical personnel has reviewed the baseline and/or modifiedCA data (with R-wave and AF markers) and other statistical informationconcerning one or more episodes experienced by the patient.Additionally, or alternatively, the sensitivity profile parameteradjustments may be automatically pushed and uploaded to the ICM at theconclusion of the operations of FIG. 4, such as when the adjustment iswithin a predetermined limit.

FIG. 5 illustrates a system level diagram indicating potential devicesand networks that utilize the methods and systems herein. For example,an implantable cardiac monitoring device (ICM) 502 may be utilized tocollect a cardiac activity data set. The ICM 502 may supply the CA dataset (CA signals and DD feature markers) to various local externaldevices, such as a tablet device 504, a smart phone 506, a bedsidemonitoring device 508, a smart watch and the like. The devices 504-508include a display to present the various types of CA signals, markers,statistics, diagnostics and other information described herein. The ICM502 may convey the CA data set over various types of wirelesscommunications links to the devices 504, 506 and 508. The ICM 502 mayutilize various communications protocols and be activated in variousmanners, such as through a Bluetooth, Bluetooth low energy, WiFi orother wireless protocol. Additionally, or alternatively, when a magneticdevice 510 is held next to the patient, the magnetic field from thedevice 510 may activate the ICM 502 to transmit the cardiac activitydata set and AF data to one or more of the devices 504-508.

The processes described herein for analyzing the cardiac activity dataand/or confirm AF detection may be implemented on one or more of thedevices 504-508. Additionally, or alternatively, the ICM 502 may alsoimplement the confirmatory processes described herein. The devices504-508 may present the CA data set and AF detection statistics anddiagnostics to clinicians in various manners. As one example, AF markersmay be illustrated on EGM signal traces. AF and sinus markers may bepresented in a marker channel that is temporally aligned with originalor modified CA signals. Additionally, or alternatively, the duration andheart rate under AF may be formatted into histograms or other types ofcharts to be presented alone or in combination with CA signals.

FIG. 6 illustrates a distributed processing system 600 in accordancewith embodiments herein. The distributed processing system 600 includesa server 602 connected to a database 604, a programmer 606, a localmonitoring device 608 and a user workstation 610 electrically connectedto a network 612. Any of the processor-based components in FIG. 6 (e.g.,workstation 610, cell phone 614, local monitoring device 616, server602, programmer 606) may perform the processes discussed herein.

The network 612 may provide cloud-based services over the Internet, avoice over IP (VoIP) gateway, a local plain old telephone service(POTS), a public switched telephone network (PSTN), a cellular phonebased network, and the like. Alternatively, the communication system 612may be a local area network (LAN), a medical campus area network (CAN),a metropolitan area network (MAN), or a wide area network (WAM). Thecommunication system 612 serves to provide a network that facilitatesthe transfer/receipt of data and other information between local andremote devices (relative to a patient). The server 602 is a computersystem that provides services to the other computing devices on thenetwork 612. The server 602 controls the communication of informationsuch as cardiac activity data sets, bradycardia episode information,asystole episode information, AF episode information, markers, cardiacsignal waveforms, heart rates, and device settings. The server 602interfaces with the network 612 to transfer information between theprogrammer 606, local monitoring devices 608, 616, user workstation 610,cell phone 614 and database 604. The database 604 stores informationsuch as cardiac activity data, AF episode information, AF statistics,diagnostics, markers, cardiac signal waveforms, heart rates, devicesettings, and the like, for a patient population. The information isdownloaded into the database 604 via the server 602 or, alternatively,the information is uploaded to the server 602 from the database 604. Theprogrammer 606 may reside in a patient's home, a hospital, or aphysician's office. The programmer 606 may wirelessly communicate withthe ICM 603 and utilize protocols, such as Bluetooth, GSM, infraredwireless LANs, HIPERLAN, 3G, satellite, as well as circuit and packetdata protocols, and the like. Alternatively, a telemetry “wand”connection may be used to connect the programmer 606 to the ICM 603. Theprogrammer 606 is able to acquire ECG from surface electrodes on aperson (e.g., ECGs) 622, electrograms (e.g., EGM) signals from the ICM603, and/or cardiac activity data, AF episode information, AFstatistics, diagnostics, markers, cardiac signal waveforms, atrial heartrates, device settings from the ICM 603. The programmer 606 interfaceswith the network 612, either via the Internet, to upload the informationacquired from the surface ECG unit 620, or the ICM 603 to the server602.

The local monitoring device 608 interfaces with the communication system612 to upload to the server 602 one or more of cardiac activity dataset, AF episode information, AF statistics, diagnostics, markers,cardiac signal waveforms, heart rates, sensitivity profile parametersettings and detection thresholds. In one embodiment, the surface ECGunit 620 and the ICM 603 have a bi-directional connection 624 with thelocal RF monitoring device 608 via a wireless connection. The localmonitoring device 608 is able to acquire cardiac signals from thesurface of a person, cardiac activity data sets and other informationfrom the ICM 603, and/or cardiac signal waveforms, heart rates, anddevice settings from the ICM 603. On the other hand, the localmonitoring device 608 may download the data and information discussedherein from the database 604 to the surface ECG unit 620 or the ICM 603.

The user workstation 610 may be utilized by a physician or medicalpersonnel to interface with the network 612 to download cardiac activitydata and other information discussed herein from the database 604, fromthe local monitoring devices 608, 616, from the ICM 603 or otherwise.Once downloaded, the user workstation 610 may process the CA data inaccordance with one or more of the operations described above. The userworkstation 610 may upload/push settings (e.g., sensitivity profileparameter settings), ICM instructions, other information andnotifications to the cell phone 614, local monitoring devices 608, 616,programmer 606, server 602 and/or ICM 603. For example, the userworkstation 610 may provide instructions to the ICM 603 in order toupdate sensitivity profile parameter settings when the ICM 603 declarestoo many false AF detections.

The processes described herein in connection with analyzing cardiacactivity data for confirming or rejecting AF detection may be performedby one or more of the devices illustrated in FIG. 6, including but notlimited to the ICM 603, programmer 606, local monitoring devices 608,616, user workstation 610, cell phone 614, and server 602. The processdescribed herein may be distributed between the devices of FIG. 6.

FIG. 7 illustrates examples of communication sessions between the ICM, alocal external device, a remote device and a server/database inaccordance with embodiments herein. For convenience, reference is madeto the devices of FIGS. 5 and 6, in connection with FIG. 7. For example,the local device may represent a cell phone 614, smart phone 506,bedside monitor 508 or local monitoring device 608, 616, while theremote device may represent a workstation 610, programmer 606, or tabletdevice 504.

During an AF detection and confirmation session 701, at 702, an ICM 100provides a CA data set to a local device. At 704, the local deviceutilizes the EFR and confirmatory feature detectors processes describedherein to analyze at least a portion of the CA signals to identify falseAF detection. The false AF detections are used to generate or update aconfirmation log 706. As described herein, the confirmation log 706 mayinclude a log of the “false positive” episode counts from the originalCA data set. The confirmation log 706 may also include correctivecharacterizations of individual events that were mischaracterized in theoriginal CA data.

In certain instances, it may be desirable to return the confirmation log706 information to the ICM as denoted at 703. In certainimplementations, an ICM is provided with certain security features thatprevent an external device (e.g., cell phone or local monitoring device)from directly changing sensitivity profile parameter settings and/orwriting to any or at least certain sections of the memory within theICM. For example, the security features may prevent an external devicefrom writing over-sensitivity profile parameter settings and/or over theAF statistics and diagnostics that are generated and stored on the ICM.

Optionally, as a workaround, at 703, the confirmation log 706 may bewritten to a more flexible section of memory within the ICM (alsoreferred to as an external device accessible section), along with headerand/or metadata information tying the confirmation log 706 to aparticular portion of the CA data. Additionally, or alternatively, at704, the local external device may pass the confirmation log 706 to oneor more remote devices and optionally to the database and server. Theconfirmation log 706 may be written to memory of an external device thatinteracts directly and regularly with the ICM, such as cell phone 614,local monitoring device 608, 616 and the like. The confirmation log 706may be associated with particular CA data sets, such as based on time ofdata acquisition.

Optionally, a remote pairing session 708 may be performed between CAdata on an ICM and locally externally stored confirmation logs. Forexample, the local external device may be directed to initiate a datatransfer/download from the ICM, such as at 710, at a point in timeseparate from and after performing the AF detection confirmationprocesses described herein. The local external device receives the CAdata set at 712 and determines, at 714, that the CA data set has alreadybeen analyzed to confirm AF detection. At 716, the local external deviceidentifies a confirmation log stored at the local external device thatcorresponds to the CA data set, and at 716, appends the confirmation logto the associated CA data set, such as based on time of dataacquisition. The cumulative information of the CA data set andconfirmation log are transferred, through the external device, to aremote server 602, database 604, workstation 610, programmer 606 orotherwise.

By maintaining the confirmation log, for a particular CA data set at thelocal external device in association with the original CA data set,remote devices (e.g., programmer 606, server 602, etc.) receive andprocess both the original CA data set and the confirmation log. Theremote device obtains the “traditional” device diagnostic sections, andis also afforded additional information from the confirmation log and isable to account (at 718) for cumulative adjustments/adjudications is AFdetection before displaying a consolidated set of AF statistics anddiagnostics to a physician or medical personnel.

Additionally, or alternatively, the operations of FIG. 7 may beimplemented in connection with remotely stored confirmation logs, suchas in communication sessions 720. At 722, a remote device may request CAdata from a particular ICM by conveying a corresponding request to alocal external device associated with the corresponding ICM. The localexternal device forwards the data request, at 724, to the ICM, inresponse thereto, at 726, the ICM transmits the CA data set to the localexternal device. The local external device forwards the CA data set, at728, to the remote device. Optionally, before relaying the CA data set,at 728, the local external device may first determine whether the CAdata set has first been analyzed for AF detection confirmation. In theexample at 720, it is presumed that the CA data set has already beenanalyzed for AF detection confirmation and thus the local externaldevice need not perform the confirmation analysis at this time.Additionally, or alternatively, the remote device may include, in therequest, a direction to the local external device to not perform AFdetection confirmation (e.g., the remote device knows that in AFdetection confirmation has already been performed and stored elsewhere).

In connection with or separate from the request for CA data set at 722,the remote device conveys a request, at 730, to a server and databasefor any confirmation logs related to the requested CA data set. Therequested may be broadcast to multiple external devices on the networkor directed to a particular server/database known to maintaininformation in connection with the particular ICM. Additionally, oralternatively, the remote device may hold the request, at 730, untilafter receiving the CA data set, at 728. For example, once a remotedevice receives the CA data set, at 728, the remote device may include,within the request for confirmation logs, an indication of the time anddate at which the CA data set was collected. In response to the request,the server and database return, at 732, one or more confirmation logs(if present). Thereafter, the remote device combines the CA data set andconfirmation log to present a consolidated summary of the data to aphysician or other medical personnel.

In connection with embodiments herein, the cloud-based approach allowsan AF episode that is detected by the ICM using the traditionaldetection algorithms, to be passed through the local external device andstored at the server 602, database 604, workstation 610 or at anotherremote device within the cloud-based system. When an individual ICM isinterrogated for a CA data set, the interrogation device would alsorequest, from the cloud-based system, any additional information, suchas any confirmation logs stored elsewhere within the system. Forexample, when an external device, such as a cell phone 614, localmonitoring device 608, 616 and/or programmer 606 interrogate anindividual ICM, the cell phone 614, local monitoring device 608, 616and/or programmer 606 would also broadcast an ICM data supplementrequest over the cloud-based system. The ICM data supplement requestrequests additional data/information related to the individual ICM(e.g., based on the ICM serial number). In response thereto, the server602 and/or other remote system may provide, to the requesting device,one or more confirmation logs or other information regarding pastoperation of the ICM. The requesting device then combines the CA dataset from the ICM with related data (e.g., a confirmation log associatedwith a particular AF episode and/or group of cardiac events) from anexternal source. The external devices pulls data from the cloud inconnection with ICM interrogation, and combine the CA data from the ICMwith any corrective or confirmation data from the log, before presentinga consolidated data summary to a physician or medical personnel.

III. PVC DETECTION PROCESS

FIG. 8 illustrates a process for detecting a presence of prematureventricular contractions (PVCs) within cardiac activity signals inaccordance with embodiments herein. In accordance with embodimentsherein, the operations of FIG. 8 are implemented in real time by an IMDto process real-time cardiac activity signals in connection withdetecting PVCs. The IMD may represent a ICM that is configured toanalyze cardiac activity signals, declare arrhythmia episodes from theCA signals, and wireless transmit the CA data sets to an externaldevice. Optionally, the IMD may represent a pacemaker, defibrillator,cardioverter, neurostimulator or other implantable device configured todelivery therapy. Additionally, or alternatively, the process of FIG. 8may be implemented as part of a “second pass” or “confirmation process”to confirm a prior declaration of an AF episode by an IMD. When used ina confirmation process, the process of FIG. 8 operations upon a CA dataset that was previously generated at the IMD to include CA signals andfeature markers. The IMD analyzes the CA signals for a sinus feature,such as R-wave features. The CA signals are appended with a markerchannel that includes features markers (e.g., sinus and/or arrhythmiamarkers such as R-wave markers, AF markers, etc. annotated by theimplanted device). The feature markers are stored in connection with theCA signals to form a CA data set. The feature markers may be identifiedin connection with an arrhythmia detection process that is implementedby the ICM, by a local external device and/or by a remote server.

At 802, the one or more processors obtain and filter CA signals. By wayof example, the processors may obtain the CA signals by at least one ofi) accessing memory of an external device or remote server where the CAsignals, information, etc. are stored, ii) receiving the CA signals overa wireless communications link between the IMD and a local externaldevice, and/or iii) receiving the data CA signals at a remote serverover a network connection. The obtaining operation, when from theperspective of an IMD, may include sensing new signals in real time,and/or accessing memory to read stored CA signals from memory within theIMD. The obtaining operation, when from the perspective of a localexternal device, includes receiving the CA signals at a transceiver ofthe local external device where the CA signals are transmitted from anIMD and/or a remote server. The obtaining operation may be from theperspective of a remote server, such as when receiving the CA signals ata network interface from a local external device and/or directly from anIMD. The remote server may also obtain the CA signals from local memoryand/or from other memory, such as within a cloud storage environmentand/or from the memory of a workstation or clinician externalprogrammer.

By way of example, at 802, the one or more processors may apply apredetermined bandpass filter, such as a digital finite impulse response(FIR) bandpass filter, to filter the incoming CA signals. A shape of thebandpass filter may be defined by setting filter coefficients such thatthe filter suppresses low-frequency components (e.g., P waves and Twaves) and suppresses high-frequency components (e.g., myopotentialnoise). The filter coefficients may be chosen to define a shape for thefilter that preserves a morphology of intermediate frequency components,such as associated with a morphology of a PVC. At 802, the one or moreprocessors rectify the filtered CA signal in order to convert allnegative signal components to positive signal components for formrectified CA signals.

At 806, the one or more processors analyze a QRS complex for each beator event within the rectified CA signals to identify a QRS feature ofinterest and a timing of the QRS feature of interest. For example, theQRS feature of interest and timing may correspond to as a peak of theQRS complex and a timing of the QRS complex peak (T_(QRS_PEAK)).

At 808, the one or more processors identify QRS segments under severebaseline drift and exclude them from further analysis. For example, theprocessors may calculate, for each cardiac beat, an average signal levelfor the CA signal over the corresponding cardiac beat. For example, theprocessors may define a window that is centered over an individual beat,such as centered at the peak of the R wave. The window may have apredefined duration, such as 160 ms (e.g., 80 msec pre and 80 msec postQRS peak). The processors calculate the average for the QRS signalwithin the window and then step the averaging window to the next beat orQRS complex. The processors calculate the average at each beat byapplying the averaging operation across the each QRS complex in the CAsignals (e.g., across 840 beats over a 80 second EGM strip). Theprocessors then determine whether to retain or reject an individual beatfrom further analysis by comparing the corresponding average to a driftthreshold. The drift threshold may be defined in various manners, basedupon the nature of the CA signal. For example, when the CA signalrepresents a voltage measurement that varies in millivolts in responseto electrical potentials, the drift threshold may be defined as amillivolt threshold (e.g., 0.13 mV). The processors utilize the driftthreshold to filter out QRS complex segments that exhibit asignificantly high QRS average due to baseline drifting. For example,the CA signals will exhibit baseline drift when one or more electrodesutilized by the IMD experience separation from adjacent tissue (loss ofcontact) and/or fluid buildup at the tissue-electrode interface.Baseline drift may occur for other reasons as well and, if notcorrected, will result in significant shifts in the amplitude of the QRSscore QRS_(SUM) for one or more beats.

FIG. 9B illustrates a CA signal 930 that includes a series of QRScomplex segments 932-934 having R-waves as noted at 938-940. The QRScomplex segments 933 and 934 exhibit substantial baseline drift 936,937, as noted by the dashed lines. The baseline drift 936, 937 causesthe QRS complex segments 933, 934 to yield QRS scores (QRS_(SUM)) thatare substantially larger than a normal QRS score associated with anormal QRS complex segment 932.

Returning to FIG. 8, at 808, the one or more processors reject the beatsor cardiac events that have QRS complex segments that exhibitsubstantial baseline drift. For example, the one or more processors mayperform baseline drift rejection, beat by beat, based on an average(QRS_(AVG)) exhibited across each corresponding QRS complex. Forexample, the average may be obtained by the processors utilizing awindow of determined length (e.g., 160 ms window starting 80 ms before aQRS peak and continuing until 80 ms after the QRS peak). An average iscalculated for each QRS complex and is compared by the processors to adrift threshold (e.g., QRS_(AVG)>0.13). When the average for anindividual QRS complex segment exceeds the drift threshold, theprocessors determine that the corresponding beat exhibits sufficient(e.g., are corrupt) baseline drift to justify the processorsremoving/rejecting the QRS complex from further analysis. The operationsat 810 remove baseline drift corrupted QRS complex segments, to form adrift corrected CA signal comprised solely of QRS complex segments thatdo not exhibit any baseline drift or exhibit a small amount of baselinedrift insufficient to classify the corresponding beat as corrupt.

Additionally, or alternatively, the processors may apply a modifiedprocess to analyze QRS complex segments located at a beginning and anend of a CA signal strip (e.g., 1 second clip of EGM signals). Theprocessors may exclude the QRS complex segments in the beginning and endportions of the CA signal strip.

Optionally, different threshold values may be utilized for baselinedrift in order to modulate a sensitivity for signal drift detection.Optionally, the processors may apply a moving average that extendsacross adjacent QRS complex segments and compare the moving average tothe drift threshold to determine whether to reject individual QRScomplex segments.

At 810, the one or more processors calculate a QRS score (QRS_(SUM))within a QRS complex segment for each of the remaining beat over of theCA signal. The QRS score is obtained, beat by beat, for each individualbeat. The QRS score may be calculated for an individual beat by summinga series of amplitudes along the corresponding QRS complex segment. Theprocessors sums amplitudes at evenly spaced sample points along the QRScomplex segment to calculate the QRS score. Optionally, the summation ofamplitudes may represent peak amplitude or peak slope within the windowdefined by the pre-and post-peak time durations. Additionally, oralternatively, the processors may calculate an energy of the QRS complexsegment.

A location and length of the QRS complex segment is based on a timing ofpeaks in the QRS complex. For example, for each beat, a QRS complexsegment is defined by a window that begins a pre-peak time durationbefore and ends a post—peak time duration after the peak of the QRScomplex. A length of the pre-and post-peak time durations may be variedin connection with adjusting a sensitivity of the overall PVC detectionprocess. When it is desirable to increase the PVC detection sensitivity,the overall length of the pre-and post-peak time durations may beincreased. Alternatively, when it is desirable to decrease the PVCdetection sensitivity, the overall length of the pre-and post-peak timedurations may be decreased. By way of example only, each of the pre-andpost-peak time durations may be set to between 80 and 80 ms, and asanother example to approximately 50 ms.

FIG. 9A illustrates a portion of a CA signal 902 (e.g., an EGM strip) inconnection with a single cardiac event or beat that is analyzed inconnection with the process of FIG. 8. The CA signal 902 includes a QRScomplex segment 904 that has a QRS complex peak 906 at a time 908. A QRSstart time 910 precedes the QRS complex peak 906 (T_(QRS_Peak)) by thepre-peak time duration 912 (Delta), while a QRS end time 914 follows theQRS complex peak 906 by the post-peak time duration 916 (Delta). At 808,the one or more processors calculate a QRS score (e.g., amplitude sums,energy, peak amplitude or peak slope) for the QRS complex 918. Forexample, a QRS score of the CA signal segment (QRS_(SUM)) from(T_(QRS_Peak)−Delta) to (T_(QRS_Peak)+Delta) is obtained. By way ofexample, the processors may perform a summing operation over the QRScomplex 918, whereby a sum is obtained for an amplitude of the QRScomplex 918 at multiple discrete points along the QRS complex 918, asdenoted by dashed lines 920. It is recognized that other techniques maybe utilized to calculate the QRS score for the QRS complex 918, and thatmore or fewer discrete points may be included within the summation.

Returning to 810, the processors calculate the multiple QRS score valuesfor multiple QRS complex segments of the CA signal over correspondingmultiple cardiac cycles. For example, a 30 second segment of CA signalsmay include 340 beats or cardiac events and thus potentially acorresponding 340 QRS complex segments.

The processors calculate correlation coefficients between QRSmorphologies of individual QRS complex segments and a QRS morphologytemplate. For example, one or more standard morphologies may be definedas a QRS morphology template. The ensemble average of all QRS complexsegments in the current CA signal can be used as a QRS morphologytemplate. The processors compare the morphology of each QRS morphologysegment to the QRS morphology template to derive a correspondingmorphology correlation coefficient.

At 812, the one or more processors compute a variability metric for theQRS score (calculated at 810) from the drift corrected CA signal. Forexample, the processors may calculate the variability metric as acovariance of the QRS score. The covariance is based on the QRS complexsegments that were not corrupted with baseline drift (e.g., did notexhibit a moving average that exceeded the drift threshold). Thecovariance Cov_QRS_(SUM) may represent a standard deviation of the QRSscore QRS_(SUM) for the QRS complex segments divided by the average ofthe QRS score QRS_(SUM) for the QRS complex segments(Cov_QRS_(SUM)=standard deviation(All QRS_(SUM))/average(AllQRS_(SUM))).

The present example for the operations at 810-812 concern calculatingQRS scores. Additionally, or alternatively, flow may move to 814, 816where the one or more processors may compare morphologies of the QRScomplex segments to one or more morphology templates. The comparison tothe morphology templates provides morphology correlation coefficientsthat may be utilized as QRS scores for the corresponding QRS complexsegments.

At 814, the one or more processors calculate a QRS complex template,namely a morphology ensemble from the QRS complex segments. For example,the morphology ensemble may represent an ensemble average of the QRSmorphologies for all or a desired number of the drift corrected QRScomplex segments.

At 816, the one or more processors compare each of the QRS morphologiesagainst the QRS complex template (e.g., ensemble averaged QRSmorphology). For example, the processors may determine and evaluate acorrelation coefficient of each of the QRS morphologies against anensemble averaged QRS morphology. The ensemble averaged QRS morphologyis obtained using a subset of the QRS complex segments, namely the QRScomplex segments that have a QRS score in a select range (e.g., aninterquartile range such as the middle 50%).

At 818, the one or more processors determine whether the variabilitymetric satisfies a first condition. For example, the first condition mayrepresent a relatively high threshold for the covariance of the QRScomplex segments. For example, the processors may determine whether thecovariance Cov_QRS_(SUM) is greater than a PVC variability threshold(Thresh_(PVC)). By way of example, the PVC variability threshold may be0.2, although other values may be utilized to modulate the sensitivityof the PVC detection. When the variability metric does not exceed thethreshold, flow moves to 826. Otherwise, when the variability metricdoes exceed the threshold, flow moves to 820. The PVC variabilitythreshold represents an extent to which the standard deviation andaverage for the QRS score may vary from one another while stillrepresenting sinus behavior, or at least a level of PVC burden that isbelow a burden threshold. Accordingly, at 826, the processors declarethe CA signals to have little or non-significant PVC burden and theprocess of FIG. 8 ends.

At 820, the one or more processors determine whether the averagecorrelation coefficient is less than a predetermined threshold (e.g.,0.92). When the average correlation coefficient is below the threshold,flow moves to 822. Alternatively, when the average correlationcoefficient exceeds the threshold, flow advances to 826. At 820, theprocessors analyze correlation characteristics based on comparisons ofQRS morphologies to QRS morphology templates. At 826, the processorsdeclare the CA signals to have little or non-significant PVC burden andthe process of FIG. 8 ends.

At 822, the one or more processors determine whether all or apredetermined number of the QRS complex segments have a minimumcorrelation coefficient that is below a minimum threshold. For example,the processors may determine whether all of the QRS complex segmentshave a minimum correlation coefficient of less than 0.75. When the QRScomplex segments have a minimum correlation coefficient that is lessthan the minimum threshold, flow advances to 826, where the processorsdeclare that the CA signals exhibit nonsignificant PVC burden. When flowadvances to 826, the processors determine that, to the extent PVC burdenexist, the presence of PVCs non-insignificant. A nonsignificant level ofPVCs may be present, such as when R wave morphologies are significantlyconsistent with each other and the morphology variance is likely due toamplitude changes. Different thresholds may be utilized in connectionwith analyzing average and minimum correlation coefficients in order tomodulate the sensitivity.

Alternatively, when the processors determine at 822 that the QRS complexsegments have a minimum correlation coefficient that equals or isgreater than the minimum threshold, flow advances to 824. At 824, theone or more processors declare that the CA signals exhibit significantPVC burden.

At 828, the one or more processors reject a prior device documentedarrhythmia (e.g., AF) that was declared by the ORI process of the ICM orIMD. For example, with reference to the operations of FIG. 3, at 828,when the CA signals are declared to have significant PVC burden, the oneor more processors may reject the original detection of AF episode.Additionally, or alternatively, the one or more processors may excludeany beats that that are identified as PVC beats and reevaluate the CAsignals for AF while excluding the identified PVC beats. The operationsat 820 and 822 avow the processors to analyze consistency within the QRScomplex signals. If QRS complex segments are not consistent, inaccordance with the thresholds at 820, 822, the processors declare apresence of PVCs in the cardiac signal.

Additionally, or alternatively, in detecting AF, R-R interval stabilitymay need to be re-evaluated excluding those QRS complexes detected asPVCs because significant R-R interval instability may still be caused bynon-PVC QRS complexes. For instance, if the covariance of RR intervalsis <0.12 excluding PVCs, the original AF diagnostic may be reversed.Alternatively, if Cov_QRS_(SUM) is very high (e.g., greater than2×Thresh_(PVC)), the original AF diagnostic may be reversed withoutanalyzing the covariance of RR intervals.

FIG. 9C illustrates multiple panels of a CA signal strip (e.g., EGMsignals over a 39 second interval) analyzed in accordance withembodiments herein. In the CA signal strip, a portion of the QRS complexsegments have been rejected as exhibiting excessive PVC burden inaccordance with the operations of FIG. 8. The circles 970 indicatedevice documented R waves, wherein the IMD has determined that an R-waveis present in the CA signals. As explained herein, when excessive PVCsoccur, the IMD may identify false positives, namely features of the CAsignals to represent R-waves when an R-wave is in fact not present. Thevertical ones 972 indicate PVCs detected by the process of FIG. 8. Whenthe IMD declares false R-waves, the IMD will also determine an RRinterval that is not necessarily representative of the patient's cardiacactivity. The false R-waves may lead the IMD to identify irregularitiesin the RR interval which in turn leads the IMD to declare a false AFepisode. For example, a false AF episode may be declared due to R-Rinterval instability introduced by PVCs as PVCs tend to introduce ashort RR interval followed by long RR interval.

The operation at and after 816 may vary based on whether the process ofFIG. 8 is implemented as a first pass detection algorithm or asecond/confirmation pass detection algorithm. As a first pass detectionalgorithm, the process of FIG. 8 analyzes CA signals for a first timewithout prior analysis by an IMD or otherwise. For example, the PVCdetection process of FIG. 8 may be implemented by an IMD as a part of,or in parallel with, an arrhythmia detection algorithm. The IMD mayapply the PVC detection first and then apply the arrhythmia detectionalgorithm.

Alternatively, as a second/confirmation detection algorithm, the processof FIG. 8 analyzes CA signals that have already been analyzed by the IMDand labeled with event markers. During a second/confirmation detectionprocess, at 816, the one or more processors may confirm a priordetermine by the IMD (e.g., confirm arrhythmia, confirm sinus behavior).

Closing

The various methods as illustrated in the Figures and described hereinrepresent exemplary embodiments of methods. The methods may beimplemented in software, hardware, or a combination thereof. In variousof the methods, the order of the steps may be changed, and variouselements may be added, reordered, combined, omitted, modified, etc.Various of the steps may be performed automatically (e.g., without beingdirectly prompted by user input) and/or programmatically (e.g.,according to program instructions).

Various modifications and changes may be made as would be obvious to aperson skilled in the art having the benefit of this disclosure. It isintended to embrace all such modifications and changes and, accordingly,the above description is to be regarded in an illustrative rather than arestrictive sense.

Various embodiments of the present disclosure utilize at least onenetwork that would be familiar to those skilled in the art forsupporting communications using any of a variety ofcommercially-available protocols, such as Transmission ControlProtocol/Internet Protocol (“TCP/IP”), User Datagram Protocol (“UDP”),protocols operating in various layers of the Open System Interconnection(“OSI”) model, File Transfer Protocol (“FTP”), Universal Plug and Play(“UpnP”), Network File System (“NFS”), Common Internet File System(“CIFS”) and AppleTalk. The network can be, for example, a local areanetwork, a wide-area network, a virtual private network, the Internet,an intranet, an extranet, a public switched telephone network, aninfrared network, a wireless network, a satellite network and anycombination thereof.

In embodiments utilizing a web server, the web server can run any of avariety of server or mid-tier applications, including Hypertext TransferProtocol (“HTTP”) servers, FTP servers, Common Gateway Interface (“CGI”)servers, data servers, Java servers, Apache servers and businessapplication servers. The server(s) also may be capable of executingprograms or scripts in response to requests from user devices, such asby executing one or more web applications that may be implemented as oneor more scripts or programs written in any programming language, such asJava®, C, C# or C++, or any scripting language, such as Ruby, PHP, Perl,Python or TCL, as well as combinations thereof. The server(s) may alsoinclude database servers, including without limitation thosecommercially available from Oracle®, Microsoft®, Sybase® and IBM® aswell as open-source servers such as MySQL, Postgres, SQLite, MongoDB,and any other server capable of storing, retrieving and accessingstructured or unstructured data. Database servers may includetable-based servers, document-based servers, unstructured servers,relational servers, non-relational servers or combinations of theseand/or other database servers.

The environment can include a variety of data stores and other memoryand storage media as discussed above. These can reside in a variety oflocations, such as on a storage medium local to (and/or resident in) oneor more of the computers or remote from any or all of the computersacross the network. In a particular set of embodiments, the informationmay reside in a storage-area network (“SAN”) familiar to those skilledin the art. Similarly, any necessary files for performing the functionsattributed to the computers, servers or other network devices may bestored locally and/or remotely, as appropriate. Where a system includescomputerized devices, each such device can include hardware elementsthat may be electrically coupled via a bus, the elements including, forexample, at least one central processing unit (“CPU” or “processor”), atleast one input device (e.g., a mouse, keyboard, controller, touchscreen or keypad) and at least one output device (e.g., a displaydevice, printer or speaker). Such a system may also include one or morestorage devices, such as disk drives, optical storage devices andsolid-state storage devices such as random access memory (“RAM”) orread-only memory (“ROM”), as well as removable media devices, memorycards, flash cards, etc.

Such devices also can include a computer-readable storage media reader,a communications device (e.g., a modem, a network card (wireless orwired), an infrared communication device, etc.) and working memory asdescribed above. The computer-readable storage media reader can beconnected with, or configured to receive, a computer-readable storagemedium, representing remote, local, fixed and/or removable storagedevices as well as storage media for temporarily and/or more permanentlycontaining, storing, transmitting and retrieving computer-readableinformation. The system and various devices also typically will includea number of software applications, modules, services or other elementslocated within at least one working memory device, including anoperating system and application programs, such as a client applicationor web browser. It should be appreciated that alternate embodiments mayhave numerous variations from that described above. For example,customized hardware might also be used and/or particular elements mightbe implemented in hardware, software (including portable software, suchas applets) or both. Further, connection to other computing devices suchas network input/output devices may be employed.

Various embodiments may further include receiving, sending, or storinginstructions and/or data implemented in accordance with the foregoingdescription upon a computer-readable medium. Storage media and computerreadable media for containing code, or portions of code, can include anyappropriate media known or used in the art, including storage media andcommunication media, such as, but not limited to, volatile andnon-volatile, removable and non-removable media implemented in anymethod or technology for storage and/or transmission of information suchas computer readable instructions, data structures, program modules orother data, including RAM, ROM, Electrically Erasable ProgrammableRead-Only Memory (“EEPROM”), flash memory or other memory technology,Compact Disc Read-Only Memory (“CD-ROM”), digital versatile disk (DVD)or other optical storage, magnetic cassettes, magnetic tape, magneticdisk storage or other magnetic storage devices or any other medium whichcan be used to store the desired information and which can be accessedby the system device. Based on the disclosure and teachings providedherein, a person of ordinary skill in the art will appreciate other waysand/or methods to implement the various embodiments.

The specification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense. It will, however, beevident that various modifications and changes may be made thereuntowithout departing from the broader spirit and scope of the invention asset forth in the claims.

Other variations are within the spirit of the present disclosure. Thus,while the disclosed techniques are susceptible to various modificationsand alternative constructions, certain illustrated embodiments thereofare shown in the drawings and have been described above in detail. Itshould be understood, however, that there is no intention to limit theinvention to the specific form or forms disclosed, but on the contrary,the intention is to cover all modifications, alternative constructionsand equivalents failing within the spirit and scope of the invention, asdefined in the appended claims.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosed embodiments (especially in thecontext of the following claims) are to be construed to cover both thesingular and the plural, unless otherwise indicated herein or clearlycontradicted by context. The terms “comprising,” “having,” “including”and “containing” are to be construed as open-ended terms (i.e., meaning“including, but not limited to,”) unless otherwise noted. The term“connected,” when unmodified and referring to physical connections, isto be construed as partly or wholly contained within, attached to orjoined together, even if there is something intervening. Recitation ofranges of values herein are merely intended to serve as a shorthandmethod of referring individually to each separate value falling withinthe range, unless otherwise indicated herein and each separate value isincorporated into the specification as if it were individually recitedherein. The use of the term “set” (e.g., “a set of items”) or “subset”unless otherwise noted or contradicted by context, is to be construed asa nonempty collection comprising one or more members. Further, unlessotherwise noted or contradicted by context, the term “subset” of acorresponding set does not necessarily denote a proper subset of thecorresponding set, but the subset and the corresponding set may beequal.

Operations of processes described herein can be performed in anysuitable order unless otherwise indicated herein or otherwise clearlycontradicted by context. Processes described herein (or variationsand/or combinations thereof) may be performed under the control of oneor more computer systems configured with executable instructions and maybe implemented as code (e.g., executable instructions, one or morecomputer programs or one or more applications) executing collectively onone or more processors, by hardware or combinations thereof. The codemay be stored on a computer-readable storage medium, for example, in theform of a computer program comprising a plurality of instructionsexecutable by one or more processors. The computer-readable storagemedium may be non-transitory.

All references, including publications, patent applications and patents,cited herein are hereby incorporated by reference to the same extent asif each reference were individually and specifically indicated to beincorporated by reference and were set forth in its entirety herein.

It is to be understood that the subject matter described herein is notlimited in its application to the details of construction and thearrangement of components set forth in the description herein orillustrated in the drawings hereof. The subject matter described hereinis capable of other embodiments and of being practiced or of beingcarried out in various ways. Also, it is to be understood that thephraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” or “having” and variations thereof herein ismeant to encompass the items listed thereafter and equivalents thereofas well as additional items.

It is to be understood that the above description is intended to beillustrative, and not restrictive. For example, the above-describedembodiments (and/or aspects thereof) may be used in combination witheach other. In addition, many modifications may be made to adapt aparticular situation or material to the teachings of the inventionwithout departing from its scope. While the dimensions, types ofmaterials and physical characteristics described herein are intended todefine the parameters of the invention, they are by no means limitingand are exemplary embodiments. Many other embodiments will be apparentto those of skill in the art upon reviewing the above description. Thescope of the invention should, therefore, be determined with referenceto the appended claims, along with the full scope of equivalents towhich such claims are entitled. In the appended claims, the terms“including” and “in which” are used as the plain-English equivalents ofthe respective terms “comprising” and “wherein.” Moreover, in thefollowing claims, the terms “first,” “second,” and “third,” etc. areused merely as labels, and are not intended to impose numericalrequirements on their objects. Further, the limitations of the followingclaims are not written in means—plus-function format and are notintended to be interpreted based on 35 U.S.C. § 112(f), unless and untilsuch claim limitations expressly use the phrase “means for” followed bya statement of function void of further structure.

What is claimed is:
 1. A computer implemented method for detectingpremature ventricular contractions (PVCs) in cardiac activity,comprising; under control of one or more processors configured withspecific executable instructions, obtaining cardiac activity (CA)signals for a series of beats; for at least a portion of the series ofbeats, calculating QRS scores for corresponding QRS complex segmentsfrom the CA signals; calculating a variability metric for QRS scoresacross the series of beats; calculating a QRS complex template using QRSsegments from the series of beats; calculating correlation coefficientsbetween the QRS complex template and the QRS complex segments, comparingthe variability metric to a variability threshold and the correlationcoefficients to a correlation threshold, and designating the CA signalsto include a predetermined level of PVC burden based on the determining.2. The method of claim 1, wherein the calculating the QRS scorescomprises calculating at least one of a summation, area under a curve,or energy for the QRS complex segments.
 3. The method of claim 1,further comprising: creating a morphology ensemble from the QRS complexsegments; and comparing the morphology ensemble to QRS morphologies forthe corresponding QRS complex segments to obtain morphology correlationcharacteristics for the QRS complex segments.
 4. The method of claim 1,wherein the computing of the variability metric includes calculating acovariance Cov_QRS from the QRS scores, the covariance based on the QRSscores.
 5. The method of claim 4, wherein the covariance Cov_QRSrepresents a standard deviation of the QRS scores of the QRS complexsegments divided by an average of the QRS scores, the QRS scoresrepresenting at least one of amplitude sums, energy or area under thecurve for the corresponding QRS complex segments.
 6. The method of claim1, wherein, when the variability metric does not satisfy the variabilitythreshold, declaring the CA signals to include an non-significant PVCburden which is less than the predetermined level of PVC burden.
 7. Themethod of claim 1, wherein the morphology ensemble represents anensemble average of the QRS morphologies for a desired number of the QRScomplex segments; and the comparing includes comparing the QRSmorphologies for the corresponding QRS complex segments to the ensembleaverage to obtain the morphology correlation characteristics.
 8. Themethod of claim 7, wherein the morphology correlation characteristicsrepresent correlations between the ensemble average and the QRSmorphologies of each of the QRS complex segments.
 9. The method of claim1, further comprising rejecting the beats from the CA signals thatexhibit a predetermined level of baseline drift.
 10. The method of claim1, when the variability metric satisfies the variability threshold andthe correlation coefficients satisfies the correlation conditions,declaring the CA signals to have significant PVC burden.
 11. A systemfor detecting premature ventricular contractions (PVCs) in cardiacactivity, comprising: memory to store executable instructions; one ormore processors that, when executing the executable instructions,configured to: obtain a cardiac activity (CA) signals for a series ofbeats; for at least a portion of the series of beats, calculate QRSscores for corresponding QRS complex segments from the CA signals;calculate a variability metric for QRS scores across the series ofbeats; calculate a QRS complex template using the QRS complex segments;calculate correlation coefficients between the QRS complex template andthe QRS complex segment; comparing the variability metric to avariability threshold and the correlation coefficients to a correlationthreshold, and designating the CA signals to include a predeterminedlevel of PVC burden based on the determining.
 12. The system of claim11, wherein the one or more processors are configured to calculate theQRS scores comprises calculating at least one of a summation, area undera curve, or energy for the QRS complex segments.
 13. The system of claim11, wherein the one or more processors are configured to create amorphology ensemble from the QRS complex segments; and compare themorphology ensemble to QRS morphologies for the corresponding QRScomplex segments to obtain morphology correlation characteristics forthe QRS complex segments, the designation based on the morphologycorrelation characteristics.
 14. The system of claim 13, wherein themorphology correlation characteristic represents an average correlationcoefficient, and wherein the determining includes determining whetherthe average correlation coefficient is smaller than the predeterminedthreshold.
 15. The system of claim 13, wherein the morphologycorrelation characteristic represents a minimum correlation coefficient,and wherein the determining includes determining whether the minimumcorrelation coefficient for the QRS complex segments is smaller than aminimum threshold.
 16. The system of claim 13, wherein the morphologyensemble represents an ensemble average of the QRS morphologies for adesired number of the QRS complex segments; and the comparing includescomparing the QRS morphologies for the corresponding QRS complexsegments to the ensemble average to obtain the morphology correlationcharacteristics.
 17. The system of claim 11, wherein the one or moreprocessors are configured to compute the variability metric bycalculating a covariance Cov_QRS_(SUM) from the QRS scores, thecovariance based on the QRS complex segments.
 18. The system of claim11, wherein, when the variability metric does not satisfy thevariability threshold, declaring the CA signals to includenon-significant PVC burden which is less than the predetermined level ofPVC burden.
 19. The system of claim 1, wherein, when the correlationcoefficients do not satisfy the correlation threshold, declaring the CAsignals do not have significant PVC burden.
 20. The system of claim 1,wherein, when the variability metric satisfied the variability thresholdand the correlation coefficients satisfy the correlation threshold,declaring the CA signals to have significant PVC burden.
 21. The systemof claim 20, wherein, when declaring the CA signals to have significantPVC burden, rejecting an original detection of AF episode, orreevaluating if the CA signals include AF by excluding identified PVCbeats.